You're correct. FaceNet, developed by Google, achieved state-of-the-art performance on the Labeled Faces in the Wild (LFW) dataset, with a reported accuracy of 99.63%. This surpasses both the 95% accuracy of earlier systems as well as DeepFace from Facebook, which achieved 97.35% accuracy on LFW. FaceNet demonstrates the rapid progress being made in deep learning for face recognition tasks.
Recent advances and challenges of digital mental healthcareYoon Sup Choi
This document discusses research analyzing the relationship between mobile phone location sensor data and measures of depressive symptom severity. The research replicated a previous study finding significant correlations between several GPS-derived features (location variance, entropy, circadian movement) and scores on the PHQ-9 depression scale. These relationships were stronger when analyzing weekend versus weekday GPS data. GPS features predicted PHQ-9 scores up to 10 weeks later, suggesting they may serve as early warning signals of depression. The findings provide further evidence that passively collected GPS data from smartphones can reliably predict depressive symptom severity.
1. Digital healthcare is coming in the form of an unavoidable tsunami.
2. Digital healthcare can be implemented through the convergence of IT, BT, and medicine to create innovations in the digital healthcare field and social value.
3. There are new waves and challenges of digital healthcare, as well as the path towards the future.
This document discusses emerging trends in the future of health care, including increased personalization and prevention, faster innovations, and new technologies like nanomedicine, regenerative medicine, and medical androids. Population growth and aging, as well as a shift toward consumer-driven health and lifestyle management, will be major drivers of change. New diagnostic tools and personalized treatments enabled by genomics, proteomics, and other converging technologies may help enhance human performance and longevity.
The document introduces Dr. Yoon-Seop Choi, a leading expert in digital healthcare in Korea. It provides details about his background, career experiences, research focus, and roles founding several digital health startups and investment organizations. The key points are:
1) Dr. Choi is a pioneer in digital healthcare in Korea, introducing the field through research, writing, and lectures.
2) He founded the Digital Healthcare Research Institute and is managing director of Digital Healthcare Partners, Korea's first digital health startup accelerator.
3) Dr. Choi has invested in and advised several prominent Korean digital health startups such as Buoyo, Jitto, and Mobile Doctor.
mHealth Israel_Hospitals and Healthcare Data_Carol Gomes_Stony Brook Universi...Levi Shapiro
Presentation by Carol Gomes, CEO / COO, Stony Brook University Hospital: Hospitals + Healthcare Data. Key Sections:
- Overview of Stony Brook Medicine Health System
- IT capital planning process
- Transition from Fee-for-Service
- Clinically Integrated Network
- Population Health Analytics Platform
- REGISTRIES – Benchmarking Quality
- Digital Transformation- Business & Clinical Capacity
- Transformation Projects: Analytics; Real-Time Health System Capabilities; Telehealth Services; Command Center Capabilities
- Command Center: Centralized Throughput Office (CTO)
- Command Throughput Office Dashboard
- Real-Time Dashboards
- Early Progress of Command Throughput Office (Boarders, Cases)
- Mobile STROKE Unit Program
- Telemedicine / TeleHealth
- Stony Brook University Hospital awarded $966,026
- Data Strategy in Decentralized Environment
- Call to Action for Startups
The document discusses opportunities and challenges in healthcare information technology. It notes that while IT is underinvested in healthcare compared to other industries, it has strong potential as a driver of higher quality care. New business models are emerging that use IT to enable more efficient workflows, new therapies, decentralized care, and pay-for-performance systems. However, the healthcare sector faces challenges of fragmentation, lack of standards, and high costs of ensuring privacy and security. Overall the document argues that IT innovation can help better align all stakeholders in healthcare and lower costs while improving outcomes.
- The traditional business model of personal genomics companies sees individuals pay to sequence their genomes and receive analysis results, while the companies keep the genomic data and sell it to pharmaceutical companies. However, this model has limitations in addressing high sequencing costs for individuals, lack of individual control over their data, and lack of incentives.
- The proposed Nebula model uses blockchain technology to connect individuals directly with data buyers, eliminating personal genomics companies as middlemen. This is intended to reduce sequencing costs for individuals, give them control over their genomic data and how it is used, and provide incentives.
- The model aims to satisfy both individuals, by addressing the above issues, and data buyers' needs around data availability, acquisition, and
The Life-Changing Impact of AI in HealthcareKalin Hitrov
For IT Leaders in the healthcare and pharmaceutical industries looking to understand the impact of AI on their industries and how to overcome the ethical and efficiency challenges that come with its use.
The document discusses the post-COVID-19 era and the partnership between the pharmaceutical industry and digital healthcare. It introduces Dr. Yoonsup Choi, a leading expert in digital healthcare in Korea who has published numerous papers and books on medical AI. He founded the first research institute on digital healthcare in Korea and co-founded a startup accelerator for healthcare companies. The COVID-19 pandemic has accelerated changes in healthcare and increased investment in digital health.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
Blood pressure market research study - November 2020Valencell, Inc
In November 2020, Valencell conducted a research study on people with hypertension to understand the personal impact of managing the disease every day and the potential for digital health solutions to improve hypertension management.
The document discusses how new companies are positioning themselves to succeed in healthcare by leveraging three major changes: improved infrastructure enables new AI-first business models, high patient engagement allows experience-focused models, and these changes will lead to more proactive care delivery and new types of jobs. It outlines how infrastructure advances like sensors, cloud computing and APIs lower barriers to entry. AI-first companies strategically build datasets and target areas like diagnostics. Experience-focused companies prioritize engagement to proactively guide patients. These shifts will drive utilization from reactive to proactive care delivered where patients are, changing the nature of healthcare jobs.
Hypertension management will change more in the next 5 years than in the last...Valencell, Inc
Why will managing hypertension change more in the next 5 years than it has in the last 100?
There are several macro trends that are driving this change:
- Hypertension is a massive global health problem (over 1B people have high BP) and it is THE leading risk factor for the global burden of disease (its a comorbidity in every major chronic disease) - more of a risk factor than tobacco, obesity, poor diet, high blood glucose, etc. - according to the WHO.
- Sensor tech - there has been no meaningful innovation in BP sensors in over 100 years. The BP cuffs in use today are fundamentally the same as the first BP cuff that came to market in the early 1900's. That’s changing now with cuffless BP sensors that are being approved by regulatory bodies.
- Care delivery – healthcare "has left the building", moving out of the hospital, into the home and everyday life. This can be seen in the huge growth in remote patient monitoring, digital therapeutics, and digital health more broadly.
- Payer models – insurance coverage is moving from fee-for-service to value-based care that’s focused on prevention and monitoring. This is particularly important in hypertension management because high BP has no outward symptoms, making the frequency and ease of BP monitoring extremely important.
Webinar: Calibration-free blood pressure monitoring using biometric earbudsValencell, Inc
Valencell was scheduled to present the results of a clinical study on its groundbreaking blood pressure monitoring technology at the American College of Cardiology conference in March, but unfortunately that conference was cancelled. So we’ve decided to share that presentation and research here in a webinar format with an open Q&A session. You can find more information on Valencell's blood pressure technology here: https://valencell.com/bloodpressure/
The 10 most innovative medical devices companies 2018insightscare
Despite these challenges, medical device companies have always been adept with the latest technology and innovations happening in the sector. Keeping this in mind, we bring you the in-depth profiles of- “The 10 Most Innovative Medical Devices Companies 2018.”
Best practices in using wearable biometric sensors to prove medical use casesValencell, Inc
The use of wearable devices in health and medical use cases is growing rapidly along with the number and capabilities of wearable devices. The sensor technology embedded in wearables today rivals the capabilities of regulatory-approved medical devices, and in many cases enables new and different use cases than we’ve seen possible before. This session will highlight the best practices we’ve seen emerging recently from real-world projects proving the efficacy of wearable devices in health and medical use cases in the areas of cardiovascular conditions, neurological disease, pain management, and other areas of interest. We’ll also explore the potential pitfalls to avoid and key things to consider when using wearables in proving out your medical use case.
AI systems have potential benefits but also risks in clinical applications. Adversarial attacks can intentionally cause models to make mistakes, and medical data is vulnerable due to limited authentication. Bias in algorithms can negatively impact patient care. Interpretability is important for trust, diagnosis, and safety issues. Frameworks are needed for developing AI with quality, safety, and accountability.
Digital Healthcare Partners (DHP) 는 디지털 헬스케어 스타트업을 육성하는 한국 유일의 전문 엑셀러레이터입니다. 최고의 디지털 헬스케어 전문가들이 의학 자문, 의료계 네트워킹, 임상 검증, 투자 유치 등에 함께 합니다. 세상을 바꿀 혁신적인 디지털 헬스케어 스타트업의 시작, DHP가 든든한 파트너가 되겠습니다. http://dhpartners.io/
'인공지능은 의료를 어떻게 혁신하는가' 주제의 2017년 11월 버전입니다.
'How Artificial Intelligence would Innovate the medicine of the future'
최윤섭 소장 (최윤섭 디지털 헬스케어 연구소)
Yoon Sup Choi, PhD (Director/Founder, Digital Healthcare Institute)
yoonsup.choi@gmail.com
Professor Yoon Sup Choi discusses digital health and the future of healthcare centered around changes in the pharmaceutical industry. He notes that three key steps in implementing digital medicine are: 1) measuring data through devices like smartphones, wearables, and genetic analysis; 2) collecting the data; and 3) gaining insights from the data using artificial intelligence. Choi also provides an overview of the digital health industry landscape and increasing investment in digital health startups from pharmaceutical companies and other investors.
Emerging Technologies Driving New Patient CareJared Johnson
The Internet of Things (IoT) and Apple Watch dominated headlines in 2015. Patients are tracking more of their own health information with wearables, and expectations are changing for how to interact with caregivers. Take an inside look at how technology is affecting patient care settings, particularly the exam room. Learn how physicians are utilizing wearables to advance medical care and engage with today's connected patients. Originally presented at the 2015 Health Care Internet Conference (HCIC).
This document discusses concepts related to the global health-tech industry. It provides an overview of key topics including the healthcare and life sciences industry in 2020, technologies and startups disrupting the status quo, a focus on the medical device industry globally and in India, and a concept note on syringe counterfeiting. The document also analyzes funding trends in 2020, highlights major disruptors like telemedicine, and provides snapshots on medical devices and new anti-counterfeiting technologies.
Asia HealthTech Investments by Julien de Salaberry (30 June 2015)KickstartPH
Kickstart Ventures' 2nd HealthTech Forum featured Julien de Salaberry, a globally-recognised expert on healthcare and technology.
Julien, the Chief Innovation Officer and Founder of The Propell Group (based in Singapore), talked about healthcare trends in Southeast Asia and how “frugal innovation" can be done in healthcare delivery.
And yeah, if you've got an interesting healthtech startup, message us at info@kickstart.ph. #startupPH
Healthcare, along with many other sectors, is facing increasing uncertainty driven by technology disruption and greater individual / patient empowerment. The barrier to entry into the sector is dropping fast enabling Asia entrepreneurs to significantly improve the Asia healthcare ecosystem
Rock Health Summit: Masterclass Making the Medical Practice of the Future a R...Daniel Kivatinos
Rock Health Summit Masterclass:
Making the Medical Practice of the Future a Reality by Daniel Kivatinos
Today’s modern medical practices are moving from outdated software to using mobile devices such as the iPad to collaborate and manage patient data efficiently. The time for change in healthcare is now and today’s tech companies are continually finding ways to help doctors save time and better communicate with their patients with better technology. Many practices are investing in healthcare technology, such as AI and machine learning, telemedicine, and payments applications. Also, the rapid adoption of consumer facing tech like Apple Health will change the way we think about our health, making us all more aware and accountable for our own care. Daniel will review in this presentation the challenges that the medical industry is facing to adopt new technologies, the solutions that are available and illustrate through real-world examples how the medical practice of the future can become a reality.
Full video of this presentation here: https://www.youtube.com/watch?v=nyBBXO-sfMk
https://www.rockhealthsummit.com/
How Healthcare is Adopting New Technologies? | 7 Best technology | CIO Women ...CIOWomenMagazine
The worldwide epidemic compelled the industry to adapt and innovate. It also described how healthcare is adopting new technologies in the following ten years.
The document provides an overview of the wearable technology market for seniors and wellness tracking. It finds that:
- The wearable market is growing rapidly and wearables for healthcare applications are projected to experience especially strong growth.
- While wearable usage among seniors is currently low, it is expected to increase substantially in coming years as designs improve to better suit seniors' needs and as Medicare potentially expands coverage.
- Fitness trackers and smartwatches are currently the most popular wearable types and wrist-worn devices dominate the market. However, seniors prefer fitness trackers over smartwatches.
- Early studies indicate wearables can provide health benefits like weight loss, reduced blood pressure, and fewer hospital admissions
The integration of mobile and medical technologiesUBMCanon
The document discusses the integration of mobile technologies and medical applications. It notes that trends like an aging population and increased chronic diseases are driving convergence between consumer mobile devices and medical applications. Smartphone usage and capabilities have grown dramatically in recent years. The document outlines how mobile technologies can help improve chronic disease management and shift healthcare delivery to the home. It covers regulatory issues for medical mobile apps and examples of integrated medical devices and apps. The future may include more integrated medical-consumer devices and sensors, as well as medical tricorder-like devices.
This document discusses a presentation by PatientKey to Samsung about integrating health devices and apps. Key points include:
- Providers can bundle devices and apps in a marketplace for patients to organize. Employers can offer discounts to employees.
- All stakeholders, including providers, patients, and employers, can see health data and results in the same place for monitoring and prioritization of patient needs.
- The second part discusses Meridian, a company partnering with PatientKey to deploy integrated health management solutions in China, leveraging their mHealth and big data platform to provide personalized healthcare services and chronic disease management.
A Roadmap for Optimizing Clinical Decision SupportHealth Catalyst
Compared to industries such as aerospace and automotive, healthcare lags behind in decision support innovation. Following the aerospace and automotive arenas, healthcare can learn critical lessons about improving its clinical decision support capabilities to help clinicians make more efficient, data-informed decisions:
Achieve widespread digitization: Healthcare must digitize its assets and operations (patient registration, scheduling, encounters, diagnosis, orders, billings, and claims) for effective CDS similarly to how aerospace digitized the aircraft, air traffic control, baggage handling, ticketing, maintenance, and manufacturing.
Build data volume and scope: Healthcare must collect socioeconomic, genomic, patient-reported outcomes, claims data, and more to truly understand the patient at the center of the human health data ecosystem.
The document discusses how healthcare is shifting from a hospital-centric model to a more distributed, data-rich and consumer-centric model driven by emerging technologies like the Internet of Things. Key factors driving this change include an aging global population, rising chronic diseases, and workforce shortages. The integration of data from various medical devices, health apps and other sources could help address inefficiencies but requires standards. The document outlines several policy principles around data standards, regulation, reimbursement and privacy to help unlock the potential of IoT and virtual care models to improve outcomes and reduce costs.
This document discusses bridging the digital health divide. It notes that while 7 in 10 U.S. adults track their health in some way, only 1 in 10 share their health data with clinicians. It also shows that the number of mobile health apps and devices has grown significantly between 2013 and 2018. The document introduces Validic as aiming to be the digital health connector, bridging the gap between individuals and their health data and clinicians by making devices more clinically accurate and data more open.
The presentation discusses the use of wearable devices and sensors for collecting data in clinical trials. While consumer wearables are becoming more common, their data may not be suitable for labeling claims with regulatory agencies without proper validation and approval. Major pharmaceutical companies are exploring how mobile health technologies can supplement traditional trial data collection methods to make trials less costly and more convenient. However, simply having FDA approval as a medical device does not guarantee its data can be used to support drug approval. Proper infrastructure, analytics, and clinical expertise is needed to incorporate sensor data into clinical trials in a way that is robust, secure, and produces scientific results.
Webinar: Digital Health Strategy: Leveraging Emerging Technologies in HealthcareIntellectsoft
WEBINAR VIDEO - https://www.intellectsoft.net/l/31/webinar-digital-healthcare
JOIN OUR WEBINAR TO:
- Explore what changed for healthcare practices and operations during COVID-19 and predict what leaders can expect in terms of recovery;
- Discover today’s featured examples of our clients’ technology solutions that can help you provide better and more efficient services;
- Discuss how to evolve and adapt for the rest of 2020 and into 2021 using emerging technologies and more efficient solutions.
BEST FOR:
- Сhief Medical Officers
- Doctors Pharmaceuticals
- HR Department Outstaffing
- Telemedicine Workers
- Insurance Companies
- Pharmacies
- Laboratories
- Private Hospitals
- Academical Health Centres
- Private Healthcare Facilities
- Management Information Systems
https://www.intellectsoft.net/
This document discusses the potential for artificial intelligence (AI) in healthcare. It begins with commonly used definitions of AI and machine learning. It then provides several examples of how AI is being used in various areas of healthcare, including assisting primary care physicians, virtual assistants, remote patient monitoring, disease screening and diagnosis, and aiding radiologists. The document discusses challenges and opportunities for AI in healthcare systems and considers perspectives on both the promise and risks of AI for improving outcomes and reducing costs. It provides an overview of the current ecosystem and stakeholders involved in developing AI for healthcare.
DigiSight is a vertically integrated mobile data management system for ophthalmology. It announced a new product suite and is collaborating with organizations on global initiatives. DigiSight provides a platform to leverage growing mobile technologies and data to improve patient care, research, and healthcare efficiencies through remote monitoring and mobile tools. It is used by practices and institutions across the US and working with the Himalayan Cataract Project internationally.
April 2013 StartUp Health Insights Funding ReportStartUp Health
See StartUp Health Insights (http://www.startuphealth.com/insights) for the most comprehensive digital health funding database. Apply to StartUp Health Academy here: http://www.startuphealth.com/about-us/application/
1) The role of health care data analysts is evolving as the volume of available data grows exponentially. With zettabytes of data being generated, analysts must make sense of both structured and unstructured information.
2) Data analytics can provide insights to improve patient outcomes, lower costs, and enhance the health care experience. Examples show how visualizing data helps health systems better understand utilization and identify at-risk patients.
3) As incentives shift from fee-for-service to value-based models, health systems must transform to focus on population health. Advanced analytics and predictive modeling will be crucial to achieving the goals of better care, lower costs, and improved health.
Similar to 디지털 헬스케어를 어떻게 구현할 것인가: 국내 스타트업 업계를 중심으로 (20)
1) The study developed a computational system called C-Path to automatically quantify over 6,600 morphological features from breast cancer epithelium and stroma in histology slides.
2) When applied to two independent patient cohorts (n=248 and n=328), a prognostic model based on the quantified features was strongly associated with patient survival, independent of other factors.
3) Three stromal features were significantly associated with survival, even more so than epithelial features, implicating tumor stroma morphology as a previously unrecognized prognostic factor for breast cancer.
This document discusses digital healthcare and artificial intelligence in medicine. It introduces Dr. Yoonsup Choi, a leading expert in digital healthcare in Korea. It details his background and accomplishments, including establishing the first research institute for digital healthcare in Korea. It also discusses his investments and advisory work with several healthcare startups. The document promotes Dr. Choi's book on medical artificial intelligence and its potential to transform the conservative medical system.
1) A digital therapeutic called reSET was the first to receive FDA approval as a prescription digital treatment for substance abuse disorders like alcohol, cocaine, and marijuana addiction. Clinical trials showed patients using reSET had statistically significant increased odds of abstinence compared to a control group.
2) The study evaluated older adults ages 60-85 who played the multitasking video game NeuroRacer. Those who received multitasking training showed reduced multitasking costs compared to control groups, performing better than untrained 20-year-olds. The training also improved neural signatures of cognitive control and benefits extended to untrained cognitive abilities.
3) Digital therapeutics can deliver evidence-based treatments through software to prevent, manage
The document discusses global trends in the digital healthcare industry and regulation. It notes that in 2018, a record $14.6 billion was invested globally in digital health, continuing a trend of annual increases since 2015. However, Korea does not have any of the 38 digital health unicorn startups valued over $1 billion that exist globally. It defines key terms like digital health, mHealth, and personal genomics. It also discusses regulatory issues and the increasing role of artificial intelligence. The future of digital medicine is that it will become integrated into ordinary medicine.
This document describes a study that used a type of deep learning called a convolutional neural network to create an algorithm for detecting diabetic retinopathy and macular edema in retinal fundus photographs. The algorithm was trained on a dataset of over 128,000 retinal images graded by ophthalmologists. It was then validated on two separate datasets, achieving high sensitivity and specificity for detecting referable diabetic retinopathy. The results demonstrate the potential for deep learning algorithms to accurately analyze medical images and help screen for diabetic eye disease.
This document discusses the use of deep learning to improve the diagnosis of breast cancer from pathology images. It describes a study where a deep learning model was trained on a large dataset of pathology slides to detect regions of breast cancer metastases. The model was able to detect cancer metastases with an accuracy of over 99%, significantly outperforming pathologists. It also reduced the time needed for analysis from hours to minutes. This demonstrates the potential for deep learning to help pathologists more accurately and efficiently diagnose cancer from digital pathology images.
The document discusses the future of healthcare and digital healthcare. It introduces Professor Yoon Sup Choi, the director of the Digital Healthcare Institute at Sungkyunkwan University. It also discusses artificial intelligence in medicine and how AI is revolutionizing the traditionally conservative medical system. However, the fast development and wide influence of medical AI is difficult for modern medical experts to understand. The document provides case studies and insights into the current state and future of medical AI.
Professor Yoon Sup Choi discusses the future of digital healthcare and insurance. He argues that the rapid development and widespread impact of medical artificial intelligence is challenging for traditionally specialized medical professionals to understand. However, this book provides a good guide by clearly explaining concepts of medical AI, its applications, and relationships with doctors. The book also analyzes perspectives on various medical AI developments, uses, and possibilities in a balanced manner.
The document summarizes the future of healthcare and digital healthcare. It introduces Professor Yoon Sup Choi, the director of the Digital Healthcare Institute at Sungkyunkwan University. It discusses how artificial intelligence is reshaping the conservative medical system and how quickly AI is developing and influencing healthcare. The convergence of information technology, biotechnology, and medicine is creating innovation that will transform medical education and clinical practice.
The document provides an overview of digital healthcare and some of the anticipated legal issues. It was written by Professor Yoon Sup Choi of Sungkyunkwan University, who is also the director of the Digital Healthcare Institute. He has experience investing in startups and advising various digital health companies. The document discusses how artificial intelligence is rapidly transforming the conservative medical system and some of the challenges this poses for medical professionals. It also briefly introduces the author's background and perspectives on digital healthcare innovation.
I do not have a definitive view on when artificial general intelligence or superintelligence may be achieved. This is an area of ongoing research and debate among experts.
Genetic deletion of HVEM in a leukemia B cell line promotes a preferential in...MARIALUISADELROGONZL
Introduction: A high frequency of mutations affecting the gene encoding Herpes
Virus Entry Mediator (HVEM, TNFRSF14) is a common clinical finding in a wide
variety of human tumors, including those of hematological origin.
Methods: We have addressed how HVEM expression on A20 leukemia cells
influences tumor survival and its involvement in the modulation of the antitumor
immune responses in a parental into F1 mouse tumor model of hybrid
resistance by knocking-out HVEM expression. HVEM WT or HVEM KO leukemia
cells were then injected intravenously into semiallogeneic F1 recipients and the
extent of tumor dissemination was evaluated.
Results: The loss of HVEM expression on A20 leukemia cells led to a significant
increase of lymphoid and myeloid tumor cell infiltration curbing tumor
progression. NK cells and to a lesser extent NKT cells and monocytes were the
predominant innate populations contributing to the global increase of immune
infiltrates in HVEM KO tumors compared to that present in HVEM KO tumors. In
the overall increase of the adaptive T cell immune infiltrates, the stem cell-like
PD-1- T cells progenitors and the effector T cell populations derived from them
were more prominently present than terminally differentiated PD-1+ T cells.
Conclusions: These results suggest that the PD-1- T cell subpopulation is likely
to be a more relevant contributor to tumor rejection than the PD-1+ T cell subpopulation. These findings highlight the role of co-inhibitory signals delivered
by HVEM upon engagement of BTLA on T cells and NK cells, placing HVEM/BTLA
interaction in the spotlight as a novel immune checkpoint for the reinforcement
of the anti-tumor responses in malignancies of hematopoietic origin.
Yoga talk & yoga slides by Flametree Yoga 11 July 2024.pdfStuart McGill
Yoga talk and yoga slides on the benefits of yoga and meditation, how it works, and how to get more very low cost yoga, or meditation, or both, in your life.
An exciting session emphasizing the timely intervention and management of obstetric sepsis for better patient outcomes.
This presentation highlights risk factors, diagnosis, management, and some interesting cases of obstetric sepsis.
Lymphoma Made Easy , New Teaching LecturesMiadAlsulami
This lecture was presented today as part of our local Saudi Fellowship program. After three years of direct interaction with trainees and hematologists, I have started to develop an understanding of what needs to be covered. This lecture might serve as a roadmap for approaching and reporting lymphoma cases.
All the information you need to know about Hypothyroidism - Introduction,
Etiology, clinical manifestations, complications, pathophysiology,
diagnosis, treatment, precautions.
These lecture slides, by Dr Sidra Arshad, offer a comprehensive look into cardiac arrhythmias.
Learning objectives:
1. Summarise how an electrocardiogram is read
2. Discuss the electrocardiographic interpretation of:
3. Abnormal voltages of the QRS complex
4. Abnormal sinus rhythms
5. Heart blocks
6. Myocardial ischemia and infarction
7. Electrolytes abnormalities
8. Explain the following terms: reentry, and circus movement
9. Describe the electrical alteration in conduction responsible for fibrillation and flutter
10. Differentiate between fibrillation and flutter based on ECG findings
11. Describe the significance of defibrillation in emergency cardiac situations
Study Resources:
1. Chapter 12, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, https://geekymedics.com/how-to-read-an-ecg/
Anthelmintics or antihelminthics are a group of antiparasitic drugs that expel parasitic worms and other internal parasites from the body by either stunning or killing them and without causing significant damage to the host. They may also be called vermifuges or vermicides
Definition of mental health nursing, terminology, classification of mental disorder, ICD-10, Indian Classification, Personality development, defense mechanism, etiology of bio psychosocial factors,
PICTURE TEST IN OBSTETRICS AND GYNAECOLOGY-Aloy Okechukwu Ugwu.pptxAloy Okechukwu Ugwu
This picture test will help medical students preparing for their final exams.
It will also be useful for resident doctors preparing for part 1 exam of National Postgraduate medical college of Nigeria and West African college of surgeons in Obstetrics and Gynaecology
General Endocrinology and mechanism of action of hormonesMedicoseAcademics
This presentation, given by Dr. Faiza, Assistant Professor of Physiology, delves into the foundational concepts of general endocrinology. It covers the various types of chemical messengers in the body, including neuroendocrine hormones, neurotransmitters, cytokines, and traditional hormones. Dr. Faiza explains how these messengers are secreted and their modes of action, distinguishing between autocrine, paracrine, and endocrine effects.
The presentation provides detailed examples of glands and specialized cells involved in hormone secretion, such as the pituitary gland, pancreas, parathyroid gland, adrenal medulla, thyroid gland, adrenal cortex, ovaries, and testis. It outlines the special features of hormones, differentiating between peptides and proteins based on their amino acid composition.
Key principles of endocrinology are discussed, including hormone secretion in response to stimuli, the duration of hormone action, hormone concentrations in the blood, and secretion rates. Dr. Faiza highlights the importance of feedback control in hormone secretion, the occurrence of hormonal surges due to positive feedback, and the role of the suprachiasmatic nucleus (SCN) of the hypothalamus as the master clock regulating rhythmic patterns in biological clocks of neuroendocrine cells and endocrine glands.
The presentation also addresses the metabolic clearance of hormones from the blood, explaining the mechanisms involved, such as metabolic destruction by tissues, binding with tissues, and excretion by the liver and kidneys. The differences in half-life between hydrophilic and hydrophobic hormones are explored.
The mechanism of hormone action is thoroughly covered, detailing hormone receptors located on the cell membrane, in the cell cytoplasm, and in the cell nucleus. The processes of upregulation and downregulation of receptors are explained, along with various types of hormone receptors, including ligand-gated ion channels, G protein–linked hormone receptors, and enzyme-linked hormone receptors. The presentation elaborates on second messenger systems such as adenylyl cyclase, cell membrane phospholipid systems, and calcium-calmodulin linked systems.
Finally, the methods for measuring hormone concentrations in the blood, such as radioimmunoassay and enzyme-linked immunosorbent assays (ELISA), are discussed, providing a comprehensive understanding of the tools used in endocrinology research and clinical practice.
Heart Valves and Heart Sounds -Congenital & valvular heart disease.pdfMedicoseAcademics
This presentation, authored by Dr. Faiza, Assistant Professor of Physiology at CIMS Multan, delivers an in-depth analysis of heart valves, heart sounds, valvular heart diseases, and congenital heart defects. It begins by distinguishing between normal and abnormal heart sounds, elucidating the timing and causes of the four heart sounds—S1, S2, S3, and S4—and their clinical significance. Detailed explanations are provided on the auscultation sounds that define conditions such as mitral stenosis, mitral insufficiency, aortic stenosis, and aortic insufficiency, with a focus on how these pathological changes affect cardiac mechanics and blood pressure.
The presentation delves into abnormal heart sounds, known as murmurs, categorizing them by their causes, which include valvular lesions, rheumatic fever, aging, congenital heart diseases, viral infections during pregnancy, and hereditary factors. It explores the various types of murmurs, their timing within the cardiac cycle, and their association with specific valvular heart diseases such as stenosis and regurgitation. The intricate relationship between systolic and diastolic murmurs and conditions like anemia and ventricular septal defects is also highlighted.
Further, the presentation covers the pathophysiology of congenital heart diseases, offering a comprehensive review of conditions such as Tetralogy of Fallot and Patent Ductus Arteriosus. It explains the mechanisms of these diseases, their impact on cardiac function, and the clinical manifestations observed in affected individuals. The physiological adjustments of the circulatory system during exercise in patients with valvular lesions are discussed, emphasizing the reduced cardiac reserve and the risk of acute pulmonary edema.
Special attention is given to the compensatory mechanisms of the heart in response to valvular diseases, including the development of concentric and eccentric hypertrophy, increased venous return, and the eventual progression to heart failure. The presentation also examines rheumatic valvular lesions, aging-related aortic stenosis, and the specific challenges posed by these conditions, such as reduced stroke volume and increased metabolic demand.
This thorough exploration of heart sounds, valvular diseases, and congenital defects is designed to enhance understanding and clinical acumen, making it a valuable resource for medical students, healthcare professionals, and educators in the field of cardiology and physiology.
Prakinsons disease and its affect on eye.Riya Bist
Enhance your knowledge about Parkinsons' disease and about basic concept that medical personnel should know regarding this topic.It is very important to know about systemic disease and its impact on the eye so, here you can learn quickly about Parkinson's disease and its ocular manifestation.Download the ppt for visualization of animation.Thank you.
12. 5%
8%
24%
27%
36%
Life Science & Health
Mobile
Enterprise & Data
Consumer
Commerce
9%
13%
23%
24%
31%
Life Science & Health
Consumer
Enterprise
Data & AI
Others
2014 2015
Investment of GoogleVentures in 2014-2015
15. What is most important factor in digital medicine?
16. “Data! Data! Data!” he cried.“I can’t
make bricks without clay!”
- Sherlock Holmes,“The Adventure of the Copper Beeches”
18. Three Steps to Implement Digital Medicine
• Step 1. Measure the Data
• Step 2. Collect the Data
• Step 3. Insight from the Data
19. Digital Healthcare Industry Landscape
Data Measurement Data Integration Data Interpretation Treatment
Smartphone Gaget/Apps
DNA
Artificial Intelligence
Telemedicine
2nd Opinion
Device
On Demand (O2O)
Wearables / IoT
3D Printer
Counseling
(ver. 1)
Digital Healthcare Institute
Diretor, Yoon Sup Choi, Ph.D.
yoonsup.choi@gmail.com
EMR/EHR
Data Platform
Accelerator/early-VC
20. Digital Healthcare Industry Landscape
Data Measurement Data Integration Data Interpretation Treatment
Smartphone Gaget/Apps
DNA
Artificial Intelligence
Telemedicine
Device
On Demand (O2O)
Wearables / IoT
3D Printer
Counseling
(ver. 0.6)
Digital Healthcare Institute
Diretor, Yoon Sup Choi, Ph.D.
yoonsup.choi@gmail.com
EMR/EHR
Data Platform
Accelerator/early-VC
38. PwC Health Research Institute Health wearables: Early days2
insurers—offering incentives for
use may gain traction. HRI’s survey
Source: HRI/CIS Wearables consumer survey 2014
21%
of US
consumers
currently
own a
wearable
technology
product
2%
wear it a few
times a month
2%
no longer
use it
7%
wear it a few
times a week
10%
wear it
everyday
Figure 2: Wearables are not mainstream – yet
Just one in five US consumers say they own a wearable device.
Intelligence Series sought to better
understand American consumers’
attitudes toward wearables through
done with the data.
PwC, Health wearables: early days, 2014
65. Human genomes are being sequenced at an ever-increasing rate. The 1000 Genomes Project has
aggregated hundreds of genomes; The Cancer Genome Atlas (TGCA) has gathered several thousand; and
the Exome Aggregation Consortium (ExAC) has sequenced more than 60,000 exomes. Dotted lines show
three possible future growth curves.
DNA SEQUENCING SOARS
2001 2005 2010 2015 2020 2025
100
103
106
109
Human Genome Project
Cumulativenumberofhumangenomes
1000 Genomes
TCGA
ExAC
Current amount
1st personal genome
Recorded growth
Projection
Double every 7 months (historical growth rate)
Double every 12 months (Illumina estimate)
Double every 18 months (Moore's law)
Michael Einsetein, Nature, 2015
67. Step1. Measure the Data
• With your smartphone
• With wearable devices (connected to smartphone)
• Personal genome analysis
... without even going to the hospital!
72. Epic MyChart App Epic EHRDatabaseDexcom App
Withings App
Dexcom CGM
Nike+
Patients Device/Apps
HealthKit EHR Hospital
Whitings
+
• Data stored in DB on the iPhone (, not mirroring to the cloud)
• Consumer controls what data goes in/out, privacy level
• HealthKit connects/direct devices, store data based on privacy rules
Apple Watch
iPhone
74. • 애플 HealthKit 가 미국의 23개 선도병원 중에, 14개의 병원과 협력
• 경쟁 플랫폼 Google Fit, S-Health 보다 현저히 빠른 움직임
• Beth Israel Deaconess 의 CIO
• “25만명의 환자들 중 상당수가 웨어러블로 각종 데이터 생산 중.
이 모든 디바이스에 인터페이스를 우리 병원은 제공할 수 없다.
하지만 애플이라면 가능하다.”
2015.2.5
84. • 약한 인공 지능 (Artificial Narrow Intelligence)
• 특정 방면에서 잘하는 인공지능
• 체스, 퀴즈, 메일 필터링, 상품 추천, 자율 운전
• 강한 인공 지능 (Artificial General Intelligence)
• 모든 방면에서 인간 급의 인공 지능
• 사고, 계획, 문제해결, 추상화, 복잡한 개념 학습
• 초 인공 지능 (Artificial Super Intelligence)
• 과학기술, 사회적 능력 등 모든 영역에서 인간보다 뛰어난 인공 지능
• “충분히 발달한 과학은 마법과 구분할 수 없다” - 아서 C. 클라크
93. 600,000 pieces of medical evidence
2 million pages of text from 42 medical journals and clinical trials
69 guidelines, 61,540 clinical trials
IBM Watson on Medicine
Watson learned...
+
1,500 lung cancer cases
physician notes, lab results and clinical research
+
14,700 hours of hands-on training
95. • Trained by 400 cases of historical patients cases
• Assessed accuracy OEA treatment suggestions
using MD Anderson’s physicians’ decision as benchmark
• When 200 leukemia cases were tested,
• False positive rate=2.9% (OEA 추천 치료법이 부정확한 경우)
• False negative rate=0.4% (정확한 치료법이 낮은 점수를 받은 경우)
• Overall accuracy of treatment recommendation=82.6%
• Conclusion: Suggested personalized treatment option
showed reasonably high accuracy
MDAnderson’s Oncology ExpertAdvisor Powered by IBM Watson
:AWeb-Based Cognitive Clinical Decision Support Tool
100. DeepFace: Closing the Gap to Human-Level
Performance in FaceVerification
Taigman,Y. et al. (2014). DeepFace: Closing the Gap to Human-Level Performance in FaceVerification, CVPR’14.
Figure 2. Outline of the DeepFace architecture. A front-end of a single convolution-pooling-convolution filtering on the rectified input, followed by three
locally-connected layers and two fully-connected layers. Colors illustrate feature maps produced at each layer. The net includes more than 120 million
parameters, where more than 95% come from the local and fully connected layers.
very few parameters. These layers merely expand the input
into a set of simple local features.
The subsequent layers (L4, L5 and L6) are instead lo-
cally connected [13, 16], like a convolutional layer they ap-
ply a filter bank, but every location in the feature map learns
a different set of filters. Since different regions of an aligned
image have different local statistics, the spatial stationarity
The goal of training is to maximize the probability of
the correct class (face id). We achieve this by minimiz-
ing the cross-entropy loss for each training sample. If k
is the index of the true label for a given input, the loss is:
L = log pk. The loss is minimized over the parameters
by computing the gradient of L w.r.t. the parameters and
Human: 95% vs. DeepFace in Facebook: 97.35%
Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people)
101. FaceNet:A Unified Embedding for Face
Recognition and Clustering
Schroff, F. et al. (2015). FaceNet:A Unified Embedding for Face Recognition and Clustering
Human: 95% vs. FaceNet of Google: 99.63%
Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people)
False accept
False reject
s. This shows all pairs of images that were
on LFW. Only eight of the 13 errors shown
he other four are mislabeled in LFW.
on Youtube Faces DB
ge similarity of all pairs of the first one
our face detector detects in each video.
False accept
False reject
Figure 6. LFW errors. This shows all pairs of images that were
incorrectly classified on LFW. Only eight of the 13 errors shown
here are actual errors the other four are mislabeled in LFW.
5.7. Performance on Youtube Faces DB
We use the average similarity of all pairs of the first one
hundred frames that our face detector detects in each video.
This gives us a classification accuracy of 95.12%±0.39.
Using the first one thousand frames results in 95.18%.
Compared to [17] 91.4% who also evaluate one hundred
frames per video we reduce the error rate by almost half.
DeepId2+ [15] achieved 93.2% and our method reduces this
error by 30%, comparable to our improvement on LFW.
5.8. Face Clustering
Our compact embedding lends itself to be used in order
to cluster a users personal photos into groups of people with
the same identity. The constraints in assignment imposed
by clustering faces, compared to the pure verification task,
lead to truly amazing results. Figure 7 shows one cluster in
a users personal photo collection, generated using agglom-
erative clustering. It is a clear showcase of the incredible
invariance to occlusion, lighting, pose and even age.
Figure 7. Face Clustering. Shown is an exemplar cluster for one
user. All these images in the users personal photo collection were
clustered together.
6. Summary
We provide a method to directly learn an embedding into
an Euclidean space for face verification. This sets it apart
from other methods [15, 17] who use the CNN bottleneck
layer, or require additional post-processing such as concate-
nation of multiple models and PCA, as well as SVM clas-
sification. Our end-to-end training both simplifies the setup
and shows that directly optimizing a loss relevant to the task
at hand improves performance.
Another strength of our model is that it only requires
False accept
False reject
Figure 6. LFW errors. This shows all pairs of images that were
incorrectly classified on LFW. Only eight of the 13 errors shown
here are actual errors the other four are mislabeled in LFW.
5.7. Performance on Youtube Faces DB
We use the average similarity of all pairs of the first one
hundred frames that our face detector detects in each video.
This gives us a classification accuracy of 95.12%±0.39.
Using the first one thousand frames results in 95.18%.
Compared to [17] 91.4% who also evaluate one hundred
frames per video we reduce the error rate by almost half.
DeepId2+ [15] achieved 93.2% and our method reduces this
error by 30%, comparable to our improvement on LFW.
5.8. Face Clustering
Our compact embedding lends itself to be used in order
to cluster a users personal photos into groups of people with
the same identity. The constraints in assignment imposed
by clustering faces, compared to the pure verification task,
Figure 7. Face Clustering. Shown is an exemplar cluster for one
user. All these images in the users personal photo collection were
clustered together.
6. Summary
We provide a method to directly learn an embedding into
an Euclidean space for face verification. This sets it apart
from other methods [15, 17] who use the CNN bottleneck
layer, or require additional post-processing such as concate-
nation of multiple models and PCA, as well as SVM clas-
102. Show and Tell:
A Neural Image Caption Generator
Vinyals, O. et al. (2015). Show and Tell:A Neural Image Caption Generator, arXiv:1411.4555
v
om
Samy Bengio
Google
bengio@google.com
Dumitru Erhan
Google
dumitru@google.com
s a
cts
his
re-
m-
ed
he
de-
nts
A group of people
shopping at an
outdoor market.
!
There are many
vegetables at the
fruit stand.
Vision!
Deep CNN
Language !
Generating!
RNN
Figure 1. NIC, our model, is based end-to-end on a neural net-
work consisting of a vision CNN followed by a language gener-
103. Show and Tell:
A Neural Image Caption Generator
Vinyals, O. et al. (2015). Show and Tell:A Neural Image Caption Generator, arXiv:1411.4555
Figure 5. A selection of evaluation results, grouped by human rating.
105. Business Area
Medical Image Analysis
VUNOnet and our machine learning technology will help doctors and hospitals manage
medical scans and images intelligently to make diagnosis faster and more accurately.
Original Image Automatic Segmentation EmphysemaNormal ReticularOpacity
Our system finds DILDs at the highest accuracy * DILDs: Diffuse Interstitial Lung Disease
Digital Radiologist
Collaboration with Prof. Joon Beom Seo (Asan Medical Center)
Analysed 1200 patients for 3 months
106. Digital Radiologist
Med Phys. 2013 May;40(5):051912. doi: 10.1118/1.4802214.
Collaboration with Prof. Joon Beom Seo (Asan Medical Center)
Analysed 1200 patients for 3 months
107. Figure 4. Participating Pathologists’ Interpretations of Each of the 240 Breast Biopsy Test Cases
0 25 50 75 100
Interpretations, %
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
66
68
70
72
Case
Benign without atypia
72 Cases
2070 Total interpretations
A
0 25 50 75 100
Interpretations, %
218
220
222
224
226
228
230
232
234
236
238
240
Case
Invasive carcinoma
23 Cases
663 Total interpretations
D
0 25 50 75 100
Interpretations, %
147
145
149
151
153
155
157
159
161
163
165
167
169
171
173
175
177
179
181
183
185
187
189
191
193
195
197
199
201
203
205
207
209
211
213
215
217
Case
DCIS
73 Cases
2097 Total interpretations
C
0 25 50 75 100
Interpretations, %
74
76
78
80
82
84
86
88
90
92
94
96
98
100
102
104
106
108
110
112
114
116
118
120
122
124
126
128
130
132
134
136
138
140
142
144
Case
Atypia
72 Cases
2070 Total interpretations
B
Benign without atypia
Atypia
DCIS
Invasive carcinoma
Pathologist interpretation
DCIS indicates ductal carcinoma in situ.
Diagnostic Concordance in Interpreting Breast Biopsies Original Investigation Research
Elmore etl al. JAMA 2015
Diagnostic Concordance Among Pathologists
Interpreting Breast Biopsy Specimens
The overall agreement between the individual pathologists’
interpretations and the expert consensus–derived reference
diagnoses was 75.3% (total 240 cases)
108. Constructing higher-level
contextual/relational features:
Relationships between epithelial
nuclear neighbors
Relationships between morphologically
regular and irregular nuclei
Relationships between epithelial
and stromal objects
Relationships between epithelial
nuclei and cytoplasm
Characteristics of
stromal nuclei
and stromal matrix
Characteristics of
epithelial nuclei and
epithelial cytoplasm
Building an epithelial/stromal classifier:
Epithelial vs.stroma
classifier
Epithelial vs.stroma
classifier
B
Basic image processing and feature construction:
H&E image Image broken into superpixels Nuclei identified within
each superpixel
A
Relationships of contiguous epithelial
regions with underlying nuclear objects
Learning an image-based model to predict survival
Processed images from patients Processed images from patients
C
D
onNovember17,2011stm.sciencemag.orgwnloadedfrom
TMAs contain 0.6-mm-diameter cores (median
of two cores per case) that represent only a small
sample of the full tumor. We acquired data from
two separate and independent cohorts: Nether-
lands Cancer Institute (NKI; 248 patients) and
Vancouver General Hospital (VGH; 328 patients).
Unlike previous work in cancer morphom-
etry (18–21), our image analysis pipeline was
not limited to a predefined set of morphometric
features selected by pathologists. Rather, C-Path
measures an extensive, quantitative feature set
from the breast cancer epithelium and the stro-
ma (Fig. 1). Our image processing system first
performed an automated, hierarchical scene seg-
mentation that generated thousands of measure-
ments, including both standard morphometric
descriptors of image objects and higher-level
contextual, relational, and global image features.
The pipeline consisted of three stages (Fig. 1, A
to C, and tables S8 and S9). First, we used a set of
processing steps to separate the tissue from the
background, partition the image into small regions
of coherent appearance known as superpixels,
find nuclei within the superpixels, and construct
Constructing higher-level
contextual/relational features:
Relationships between epithelial
nuclear neighbors
Relationships between morphologically
regular and irregular nuclei
Relationships between epithelial
and stromal objects
Relationships between epithelial
nuclei and cytoplasm
Characteristics of
stromal nuclei
and stromal matrix
Characteristics of
epithelial nuclei and
epithelial cytoplasm
Epithelial vs.stroma
classifier
Epithelial vs.stroma
classifier
Relationships of contiguous epithelial
regions with underlying nuclear objects
Learning an image-based model to predict survival
Processed images from patients
alive at 5 years
Processed images from patients
deceased at 5 years
L1-regularized
logisticregression
modelbuilding
5YS predictive model
Unlabeled images
Time
P(survival)
C
D
Identification of novel prognostically
important morphologic features
basic cellular morphologic properties (epithelial reg-
ular nuclei = red; epithelial atypical nuclei = pale blue;
epithelial cytoplasm = purple; stromal matrix = green;
stromal round nuclei = dark green; stromal spindled
nuclei = teal blue; unclassified regions = dark gray;
spindled nuclei in unclassified regions = yellow; round
nuclei in unclassified regions = gray; background =
white). (Left panel) After the classification of each
image object, a rich feature set is constructed. (D)
Learning an image-based model to predict survival.
Processed images from patients alive at 5 years after
surgery and from patients deceased at 5 years after
surgery were used to construct an image-based prog-
nostic model. After construction of the model, it was
applied to a test set of breast cancer images (not
used in model building) to classify patients as high
or low risk of death by 5 years.
www.ScienceTranslationalMedicine.org 9 November 2011 Vol 3 Issue 108 108ra113 2
onNovember17,2011stm.sciencemag.orgDownloadedfrom
Digital Pathologist
Sci Transl Med. 2011 Nov 9;3(108):108ra113
109. Digital Pathologist
Sci Transl Med. 2011 Nov 9;3(108):108ra113
Top stromal features associated with survival.
primarily characterizing epithelial nuclear characteristics, such as
size, color, and texture (21, 36). In contrast, after initial filtering of im-
ages to ensure high-quality TMA images and training of the C-Path
models using expert-derived image annotations (epithelium and
stroma labels to build the epithelial-stromal classifier and survival
time and survival status to build the prognostic model), our image
analysis system is automated with no manual steps, which greatly in-
creases its scalability. Additionally, in contrast to previous approaches,
our system measures thousands of morphologic descriptors of diverse
identification of prognostic features whose significance was not pre-
viously recognized.
Using our system, we built an image-based prognostic model on
the NKI data set and showed that in this patient cohort the model
was a strong predictor of survival and provided significant additional
prognostic information to clinical, molecular, and pathological prog-
nostic factors in a multivariate model. We also demonstrated that the
image-based prognostic model, built using the NKI data set, is a strong
prognostic factor on another, independent data set with very different
SD of the ratio of the pixel intensity SD to the mean intensity
for pixels within a ring of the center of epithelial nuclei
A
The sum of the number of unclassified objects
SD of the maximum blue pixel value for atypical epithelial nuclei
Maximum distance between atypical epithelial nuclei
B
C
D
Maximum value of the minimum green pixel intensity value in
epithelial contiguous regions
Minimum elliptic fit of epithelial contiguous regions
SD of distance between epithelial cytoplasmic and nuclear objects
Average border between epithelial cytoplasmic objects
E
F
G
H
Fig. 5. Top epithelial features. The eight panels in the figure (A to H) each
shows one of the top-ranking epithelial features from the bootstrap anal-
ysis. Left panels, improved prognosis; right panels, worse prognosis. (A) SD
of the (SD of intensity/mean intensity) for pixels within a ring of the center
of epithelial nuclei. Left, relatively consistent nuclear intensity pattern (low
score); right, great nuclear intensity diversity (high score). (B) Sum of the
number of unclassified objects. Red, epithelial regions; green, stromal re-
gions; no overlaid color, unclassified region. Left, few unclassified objects
(low score); right, higher number of unclassified objects (high score). (C) SD
of the maximum blue pixel value for atypical epithelial nuclei. Left, high
score; right, low score. (D) Maximum distance between atypical epithe-
lial nuclei. Left, high score; right, low score. (Insets) Red, atypical epithelial
nuclei; black, typical epithelial nuclei. (E) Minimum elliptic fit of epithelial
contiguous regions. Left, high score; right, low score. (F) SD of distance
between epithelial cytoplasmic and nuclear objects. Left, high score; right,
low score. (G) Average border between epithelial cytoplasmic objects. Left,
high score; right, low score. (H) Maximum value of the minimum green
pixel intensity value in epithelial contiguous regions. Left, low score indi-
cating black pixels within epithelial region; right, higher score indicating
presence of epithelial regions lacking black pixels.
onNovember17,2011stm.sciencemag.orgDownloadedfrom
and stromal matrix throughout the image, with thin cords of epithe-
lial cells infiltrating through stroma across the image, so that each
stromal matrix region borders a relatively constant proportion of ep-
ithelial and stromal regions. The stromal feature with the second
largest coefficient (Fig. 4B) was the sum of the minimum green in-
tensity value of stromal-contiguous regions. This feature received a
value of zero when stromal regions contained dark pixels (such as
inflammatory nuclei). The feature received a positive value when
stromal objects were devoid of dark pixels. This feature provided in-
formation about the relationship between stromal cellular composi-
tion and prognosis and suggested that the presence of inflammatory
cells in the stroma is associated with poor prognosis, a finding con-
sistent with previous observations (32). The third most significant
stromal feature (Fig. 4C) was a measure of the relative border between
spindled stromal nuclei to round stromal nuclei, with an increased rel-
ative border of spindled stromal nuclei to round stromal nuclei asso-
ciated with worse overall survival. Although the biological underpinning
of this morphologic feature is currently not known, this analysis sug-
gested that spatial relationships between different populations of stro-
mal cell types are associated with breast cancer progression.
Reproducibility of C-Path 5YS model predictions on
samples with multiple TMA cores
For the C-Path 5YS model (which was trained on the full NKI data
set), we assessed the intrapatient agreement of model predictions when
predictions were made separately on each image contributed by pa-
tients in the VGH data set. For the 190 VGH patients who contributed
two images with complete image data, the binary predictions (high
or low risk) on the individual images agreed with each other for 69%
(131 of 190) of the cases and agreed with the prediction on the aver-
aged data for 84% (319 of 380) of the images. Using the continuous
prediction score (which ranged from 0 to 100), the median of the ab-
solute difference in prediction score among the patients with replicate
images was 5%, and the Spearman correlation among replicates was
0.27 (P = 0.0002) (fig. S3). This degree of intrapatient agreement is
only moderate, and these findings suggest significant intrapatient tumor
heterogeneity, which is a cardinal feature of breast carcinomas (33–35).
Qualitative visual inspection of images receiving discordant scores
suggested that intrapatient variability in both the epithelial and the
stromal components is likely to contribute to discordant scores for
the individual images. These differences appeared to relate both to
the proportions of the epithelium and stroma and to the appearance
of the epithelium and stroma. Last, we sought to analyze whether sur-
vival predictions were more accurate on the VGH cases that contributed
multiple cores compared to the cases that contributed only a single
core. This analysis showed that the C-Path 5YS model showed signif-
icantly improved prognostic prediction accuracy on the VGH cases
for which we had multiple images compared to the cases that con-
tributed only a single image (Fig. 7). Together, these findings show
a significant degree of intrapatient variability and indicate that increased
tumor sampling is associated with improved model performance.
DISCUSSION
Heat map of stromal matrix
objects mean abs.diff
to neighbors
H&E image separated
into epithelial and
stromal objects
A
B
C
Worse
prognosis
Improved
prognosis
Improved
prognosis
Improved
prognosis
Worse
prognosis
Worse
prognosis
Fig. 4. Top stromal features associated with survival. (A) Variability in ab-
solute difference in intensity between stromal matrix regions and neigh-
bors. Top panel, high score (24.1); bottom panel, low score (10.5). (Insets)
Top panel, high score; bottom panel; low score. Right panels, stromal matrix
objects colored blue (low), green (medium), or white (high) according to
each object’s absolute difference in intensity to neighbors. (B) Presence
R E S E A R C H A R T I C L E
onNovember17,2011stm.sciencemag.orgDownloadedfrom
Top epithelial features.The eight panels in the figure (A to H) each
shows one of the top-ranking epithelial features from the bootstrap
anal- ysis. Left panels, improved prognosis; right panels, worse prognosis.
115. In an early research project involving 600 patient cases, the team was able to
predict near-term hypoglycemic events up to 3 hours in advance of the symptoms.
IBM Watson-Medtronic
Jan 7, 2016
126. 아무도 원하지 않는 제품을 만들고 있는 것은 아닌가?
• 진짜 니즈가 무엇인지 파악하라
• 고객들이 원한다고 말하는 것 (X)
• 고객들이 원한다고 당신이 생각하는 것 (X)
• 실제로 진짜 고객들이 원하는 것 (O)
• 무엇이 가능한지 모르기 때문에, 고객은 스스로 무엇을 원하는지 모를 것이다.
141. 의료적 관점에서도 동의할 수 있는 해결책인가
• 의료 전문가 (의사)의 조언이 필요하다.
• 과학적/의학적 설득력이 없는 (a.k.a. 사이비) 서비스/제품은 곤란하다.
• 의료 현실에 맞지 않는 서비스는 외면 당하거나, 극심한 반대에 부딪힌다.
• 직원 중에 의사가 꼭 있을 필요는 없지만, 언제든 조언을 얻을 수 있는 분은 필요하다.
• 의사들 사이에서도 성향 차이 / 의견 차이가 존재한다.
143. 한국 의료 시스템의 특수성을 이해하라
• 한국 의료 체계는 미국과는 크게 다르다.
• 국내 의료 시스템의 특성을 명확히 파악할 필요가 있다.
• 의료 접근성, 의료 보험 체계, 의료 수가 등등
• 미국에서 통했던 것이, 한국에서는 통하지 않거나 / 아예 불법일 수 있다.
• 그렇다고 꼭 국내 시장에 국한될 필요는 없다.
150. • 헬스케어/의료 서비스는 근거가 필수적이다.
• 하지만 그렇지 못한 것이 현실
applications, from photometric diagnostics to
medical-grade imaging (16).Taking advantage of
these properties, newly developed devices permit
the automated determination of refractive error
merely by having an individual look through a
lens attached to a smartphone (17). Another
transportable imaging capability involves the
enabling of remote diagnosis through the use of
a smartphone case with an attached otoscope
(for detecting an ear infection) (18), multimodal
colposcope for cervical cancer identification (19),
or optical screening tool for potentially cancerous
oral lesions (20). Dermatologic diagnostics may be
especially well suited for exploiting the myriad
smartphone capabilities for teledermatology (21).
The technologies highlighted above can improve
care simply through their ability to markedly in-
crease the accessibility and convenience of care
by bringing clinic- and hospital-quality moni-
toring and diagnostics to the point of need. How-
ever, their greatest potential might be in allowing
for the complete redefining of “normal” physio-
logical responses and in enhancing our under-
standing of the natural histories of poorly defined
chronic conditions. Continuous beat-to-beat moni-
toring of blood pressure throughout daily activities
will help to refine the catchall diagnosis of “essential hypertension” as
multiple distinct phenotypes. Similarly, understanding individual varia-
views conclude that high-quality evidence is lacking for the use of
mHealth to effect behavioral changes or to manage chronic diseases,
1000
Funding ($) in millions
Publications
Funding($inmillions)
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
800
600
400
200
0
10,000
8000
6000
4000
2000
0
WoSpublications(number)
Fig. 2. mHealth taking center stage. Measures are funding and number of related publications.
Shown are the annual total funding for patient-facing mHealth companies and the annual num-
ber of related publications [identified with Web of Science (WoS) using search terms “telemedi-
cine” and “mhealth*” and “digital health” and “digital medicine”]. Funding data provided by
B. Dolan and A. Pai of MobiHealthNews.
R E V I E W
onApril27,2015emag.org
근거를 만들어야 한다.
151. 근거를 만들어야 한다.
• 의료 기관과의 협업이 필요할 가능성이 높다.
• 하지만 의료 기관과 일하기 쉽지 않다.
• Right person, Right hospital, Right department, Right time…
• 의사의 관심과 스타트업의 관심사가 다르다.
• 의사와 스타트업의 공통점: 리소스가 턱없이 부족하다.
• 가장 좋은 근거는 역시 임상 연구 결과
• 연구 조건은 case by case.
• Randomised, Double-blinded, controlled trial.
• 충분한 N 수, 충분한 기간
153. 헬스케어는 규제 산업이다
• 규제는 본질적으로 기술의 발전을 뒤따를 수 밖에 없다.
• 국내 규제 상황은 별로 좋지 않다.
• 합리성, 일관성, 불확실성
• 싫든 좋든, 규제를 개척하는 것도 역할의 하나이다.
• 초기에 식약처 등 관련 기관을 컨택하는 것도 필요하다.
159. 헬스케어 도메인 전문가
의사/병원과의 협력
헬스케어 창업 및 exit 경험 있는 기업가
초기 투자 등 자금 조달 전문가
제조 기술 전문가 및 지원 서비스
해외 시장 개척 및 해외 투자 유치 지원
0 2 4 6 8 10 12 14 16 18
디지털 헬스케어 엑셀러레이터에게 가장 필요한 것은?
Source: Mobile Healthcare | 웨어러블 디바이스, 모바일 헬스케어
https://www.facebook.com/groups/koreamobilehealthcare/