Author Interviews, Brigham & Women's - Harvard, Fertility, Technology / 16.09.2020

MedicalResearch.com Interview with: Hadi Shafiee, PhD Assistant Professor, Harvard Medical School Brigham and Women's Hospital Department of Medicine MedicalResearch.com: What is the background for this study? What are some of the characteristics that AI uses to identify blastocysts witha better chance of successful implantation?  Response: In-vitro fertilization (IVF), while a solution to many infertile couples is still extremely inefficient with a success rate of nearly 30% and is both mentally, physically, and economically taxing to patients. The IVF process involves the insemination of eggs and the culture of embryos externally in a fertility lab before transferring the developed embryo to the mother. A major challenge in the field is deciding on the embryos that need to be transferred during IVF, such that chances of a healthy birth are maximal and any complications for both mother and child are minimal. Currently, the tools available to embryologists when making such are extremely limited and expensive, and thus, most embryologists are required to make these life-altering decisions using only their observational skills and expertise. In such scenarios, their decision-making process is extremely subjective and tends to be variable. (more…)
Author Interviews, Brigham & Women's - Harvard, Gastrointestinal Disease, Technology / 27.08.2020

MedicalResearch.com Interview with: Giovanni Traverso, MB, BChir, PhD Gastroenterologist and biomedical engineer Division of Gastroenterology at BWH Instructor of medicine at Harvard Medical School MedicalResearch.com: What is the background for this study? Response: We began working on this project with the goal to develop liquid drug formulations that could offer an easier-to-swallow alternative to capsules, especially for children. We started to think about whether we could develop liquid formulations that could form a synthetic epithelial lining that could then be used for drug delivery, making it easier for the patient to receive the medication by providing drugs in extended release formats. We discovered that an enzyme called catalase could help assemble molecules of dopamine into the polymer (poly-dopamine). These polymers have muco-adhesion properties, which means that after polymerization, the polymer can attach to the tissue very strongly. Also, catalase is found throughout the digestive tract, with especially high levels in the upper region of the small intestine. This is the first example, to the best of our knowledge, of small intestinal targeting system enabled through in-situ tissue-enzyme-catalyzed polymerization. The coating lasts up to 24 hours, after which it is shed and excreted based on experiments we conducted in pigs. (more…)
Author Interviews, Cancer Research, Nature, Technology / 06.08.2020

MedicalResearch.com Interview with: Moritz Gerstung PhD Group Leader: Computational cancer biology EMBL-European Bioinformatics Institute MedicalResearch.com: What is the background for this study? Response: We have learned a lot in the last ten years about the molecular nature about various cancers thanks to the resources created by TCGA, ICGC and many other initiatives. Similarly, digital pathology has progressed hugely due to new AI algorithms. Yet it hasn’t been explored deeply how a cancer’s genetic makeup and its histopathological appearance are related. Here computers can be very helpful as they can process large amounts of digital microscopy slide images and test whether there are any recurrent histopathological patterns in relation to hundreds or thousands of genetic and other molecular abnormalities.  (more…)
Author Interviews, Genetic Research, Nature, Technology / 15.07.2020

MedicalResearch.com Interview with: Dr.Altuna Akalin PhD Head of Bioinformatics and Omics Data Science Platform Berlin Institute for Medical Systems Biology (BIMSB) Max Delbrück Center for Molecular Medicine (MDC) Berlin, Germany  MedicalResearch.com: What is the background for this study? Where does the word Janggu come from?  Response: Deep learning applications on genomic datasets used to be a cumbersome process where researchers spend a lot of time on preparing and formatting data before they even can run deep learning models. In addition, the evaluation of deep learning models and the choice of deep learning framework were also not straightforward. To streamline these processes, we developed Janggu. With this framework, we are aiming to relieve some of that technical burden and make deep learning accessible to as many people as possible. Janggu is named after a traditional Korean drum shaped like an hourglass turned on its side. The two large sections of the hourglass represent the areas Janggu is focused: pre-processing of genomics data, results visualization and model evaluation. The narrow connector in the middle represents a placeholder for any type of deep learning model researchers wish to use.  (more…)
Author Interviews, Dermatology, Melanoma, Nature, Technology / 23.06.2020

MedicalResearch.com Interview with: Professor Harald Kittler, MD ViDIR Group, Department of Dermatology Medical University of Vienna Vienna, Austria MedicalResearch.com: What is the background for this study?  What types of skin cancers were assessed? (melanoma, SCC, Merkel etc). Response: Some researchers believe that AI will make human intelligence dispensable. It is, however, still a matter of debate how exactly AI will influence diagnostic medicine in the future. The current narrative is focused on a competition between human and artificial intelligence. We sought to shift the direction of this narrative more towards human/AI collaboration. To this end we studied the use-case of skin cancer diagnosis including the most common types of skin cancer such as melanoma, basal cell- and squamous cell carcinoma. The initial idea was to explore the effects of varied representations of AI support across different levels of clinical expertise and to address the question of how humans and machines work together as a team. (more…)
Author Interviews, Ophthalmology, Technology / 18.06.2020

MedicalResearch.com Interview with: Prof. FanProf. FAN Zhiyong PhD University of California, Irvine HKUST School of Engineering MedicalResearch.com: What is the background for this study? What are the main findings? Response: According to the report of The World Health Organization, there are over 252 million people suffering from visual impairment globally and 15 million of them are difficult to cure by conventional medical methods. However, today, even the best bionic eyes have only 200 clinical trials, less than 1 ppm of all the patients, mainly due to their poor performance and high cost. The huge gap in supply and demand triggers the study of bionic eyes with performance comparable to human eyes. One important reason for their poor performance is the mismatch in shape between the flat bionic eyes and concave sclera. To protect the soft tissue in eyes from being damaged by the bionic surface, the implanted bionic eyes have to be small. This has limited the sensing area and further the electrodes number, and finally yielded poor image sensing characters with low resolution and narrow field-of-view. In this work, we are trying to achieve high performance image sensing by biomimeticing human eyes. The high-density NWs are well aligned and embedded in a hemispherical template to serve as retina. The conformal attachment of bionic eyes with sclera enables the large sensing area and wide visual angle. In addition, each individual high-density nanowires can potentially work as an individual pixel. By addressing these challenges, our device design has huge potential to improve the image sensing performance of bionic eyes. (more…)
Author Interviews, Dermatology, Melanoma, Technology / 17.06.2020

MedicalResearch.com Interview with: Chi Hwan Lee PhD Assistant Professor of Biomedical Engineering and Mechanical Engineering, and by Courtesy, of Materials Engineering, and Speech, Language, & Hearing Sciences Purdue University  MedicalResearch.com: What is the background for this study? Response: Conventional melanoma therapies, including chemotherapy and radiotherapy, suffer from the toxicity and side effects of repeated treatments due to the aggressive and recurrent nature of melanoma cells. Less-invasive topical chemotherapies by utilizing miniaturized polymeric microneedles are emerged as an alternative, but the sustained, long-lasting release of drug cargos remains challenged due to the rapidly dissolving behavior of polymers (typically, within 15 min-2 hrs). In addition, the size of the microneedles is still large for small, curvilinear and sensitive areas of tissues such as cornea (for ocular melanoma). (more…)
Author Interviews, JAMA, Pediatrics, Social Issues, Technology / 18.05.2020

MedicalResearch.com Interview with: Pooja S. Tandon, MD, MPH Center for Child Health, Behavior and Development Seattle Children's Research Institute MedicalResearch.com: What is the background for this study? Response: Cell phone use is common among middle and high school students, yet we do not have an understanding of school cell phone policies and practices in the U.S. We conducted a survey of public schools serving grades 6-12. The survey sent to over 1,100 school principals, representing a national sample of schools across the U.S., asked questions about the presence of a cell phone policy for students and staff and restrictions on phone use. Additional questions addressed consequences of policy violation, the use of cell phones for curricular activities and principals’ attitudes toward cell phone policies. (more…)
Author Interviews, Dermatology, Technology / 03.04.2020

MedicalResearch.com Interview with: Jung-Im Na, MD PhD Associate Professor, Department of Dermatology Seoul National University Bundang Hospital Korea  MedicalResearch.com: What is the background for this study? Would you briefly explain what is meant by a convolutional neural network? Response: When a very young child looks at a picture, she can easily identify cats and dogs, however, even the most advanced computers had struggled at this task until recently. Computers began to “see” with the recent advancement of Deep Learning techniques. Deep Learning is a machine learning technique that teaches computers to learn from raw data. Most deep learning methods use artificial neural network architectures, imitating human brain, and convolutional neural networks (CNN) is a particular type of deep learning architecture, imitating the visual cortex. CNN is especially powerful for recognizing images. CNN exploit the information contained in image datasets to automatically learn features and patterns. (more…)
Author Interviews, Columbia, COVID -19 Coronavirus, NYU, Technology / 02.04.2020

MedicalResearch.com Interview with: Professor Anasse Bari PhD Courant Institute of Mathematical Sciences, Computer Science Department, New York University, New York, and Megan Coffee MD PhD Division of Infectious Diseases and Immunology, Department of Medicine New York University, Department of Population and Family Health Mailman School of Public Health Columbia University, New York MedicalResearch.com: What is the background for this study? Coffee and Bari:  This work is led by NYU Grossman School of Medicine and NYU’s Courant Institute of Mathematical Sciences, in partnership with Wenzhou Central Hospital and Cangnan People's Hospital, both in Wenzhou, China. This is a multi-disciplinary team with backgrounds in clinical infectious disease as well as artificial intelligence (AI) and computer science. There is a critical need to better understand COVID-19. Doctors learn from collective and individual clinical experiences. Here, no clinician has years of experience. All are learning as they go, having to make important decisions about clinical management with stretched resources. The goal here is to augment clinical learning with machine learning. In particular, the goal is to allow clinicians to identify early who from the many infected will need close medical attention. Most patients will first develop mild symptoms, yet some 5-8 days later will develop critical illness. It is hard to know who these people are who will need to be admitted and may need to be intubated until they become ill. Knowing this earlier would allow more attention and resources to be spent on those patients with worse prognoses. If there were ever treatments in the future that could be used early in the course of illness, it would be important to identify who would most benefit We present in this study a first step in building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. It is at this point a proof of concept that it could be possible to identify future severity based on initial presentation in COVID-19. (more…)
Author Interviews, Infections, Technology / 06.03.2020

MedicalResearch.com Interview with: Arni S.R. Srinivasa Rao, PhD Professor, Division of Health Economics and Modeling, DPHS Director - Laboratory for Theory and Mathematical Modeling Department of Medicine - Division of Infectious Diseases Medical College of Georgia Department of Mathematics, Augusta UniversityArni S.R. Srinivasa Rao, PhD Professor, Division of Health Economics and Modeling, DPHS Director - Laboratory for Theory and Mathematical Modeling Department of Medicine - Division of Infectious Diseases Medical College of Georgia Department of Mathematics, Augusta University MedicalResearch.com: What is the background for this study? What are the main findings? Response:  This is a methodological study with a flowchart, algorithm, and theory to enable quicker identification of individuals at risk of coronavirus based on CDC's guidelines on COVID-19.  (more…)
Author Interviews, BMJ, Dermatology, Technology / 13.02.2020

MedicalResearch.com Interview with: Professor Jon Deeks PhD, CStat Institute of Applied Health Research Professor of Biostatistics College of Medical and Dental Sciences University of Birmingham, UK MedicalResearch.com: What is the background for this study? Response: Skin cancer is one of the most common cancers in the world, and the incidence is increasing. In 2003, the World Health Organization estimated that between two and three million skin cancers occur globally each year, 80% of which are basal cell carcinoma, 16% cutaneous squamous cell carcinoma, and 4% melanoma. The potential for melanoma to metastasise to other parts of the body means that it is responsible for up to 75% of skin cancer deaths. Five year survival can be as high as 91-95% for melanoma if it is identified early, which makes early detection and treatment key to improving survival. Early detection of melanoma is reliant on people with new or changing moles seeking early advice from medical professionals. Skin cancer smartphone applications (“apps”) provide a technological approach to assist people with suspicious lesions to decide whether they should seek further medical attention. Of increasing interest are smartphone apps that use inbuilt algorithms (or “artificial intelligence”) that catalogue and classify images of lesions into high or low risk for skin cancer (usually melanoma). Apps with inbuilt algorithms that make a medical claim are now classified as medical devices that require regulatory approval. These apps could be harmful if recommendations are erroneous, particularly if false reassurance leads to delays in people obtaining medical assessment.  CE (Conformit Europenne) marking has been applied to allow distribution of two algorithm based apps in Europe (SkinScan and SkinVision), one of which is also available in Australia and New Zealand. However, no apps currently have United States Food and Drug Administration (FDA) approval to allow their distribution in the US and Canada. We have completed a systematic review of studies that examine the accuracy of all apps that use inbuilt algorithms to identify skin cancer in users of smartphones.  We report on the scope, findings, and validity of the evidence. (more…)
Addiction, Author Interviews, Technology / 29.01.2020

MedicalResearch.com Interview with: John W. Ayers, PhD MA Vice Chief of Innovation | Assoc. Professor Div. Infectious Disease & Global Public Health University of California San Diego MedicalResearch.com: What is the background for this study? Response: Already half of US adults use smart device enabled intelligent virtual assistants, like Amazon Alexa. Moreover, many of the makers of intelligent virtual assistants are poised to roll out health care advice, including personalized wellness strategies. We take a step back and ask do intelligent virtual assistants provide actionable health support now? To do so we focus on a specific case study. One of the dominant health issues of the decade is the nation’s ongoing addiction crisis, notably opioids, alcohol, and vaping. As a result, it is an ideal case to begin exploring the ability of intelligent virtual assistants to provide actionable answers for obvious health questions. (more…)
Alzheimer's - Dementia, Author Interviews, Cognitive Issues, McGill, Neurology, Technology / 28.01.2020

MedicalResearch.com Interview with: Yasser Iturria-Medina PhD Assistant Professor, Department of Neurology and Neurosurgery Associate member of the Ludmer Centre for Neuroinformatics and Mental Health McConnell Brain Imaging Centre McGill University MedicalResearch.com: What is the background for this study? Response: As background, two main points:
  • Almost all molecular (gene expression) analyses performed in neurodegeneration are based on snapshots data, taking at one or a few time points covering the disease's large evolution. Because neurodegenerative diseases take decades to develop, until now we didn't have a dynamical characterization of these diseases. Our study tries to overcome such limitation, proposing a data-driven methodology to study long term dynamical changes associated to disease.
Also, we still lacked robust minimally invasive and low-cost biomarkers of individual neuropathological progression. Our method is able to offer both in-vivo and post-mortem disease staging highly predictive of neuropathological and clinical alterations. (more…)
Author Interviews, JAMA, Surgical Research, Technology, University of Michigan / 13.01.2020

MedicalResearch.com Interview with: Kyle Sheetz, MD Clinical Year 4 Resident, General Surgery Michigan Medicine MedicalResearch.com: What is the background for this study? Response: There are concerns that robotic surgery is increasing for common surgical procedures with limited evidence and unclear clinical benefit. Prior studies describing the use of robotic surgery relied upon claims or billing data to identify robotic operations from laparoscopic or open ones. This may lead to inaccuracies as claims data may not provide specific codes for robotic operations. (more…)
Alzheimer's - Dementia, Author Interviews, Technology / 08.01.2020

MedicalResearch.com Interview with: Dr.med.univ. Roland Beisteiner Department of Neurology Laboratory for Functional Brain Diagnostics and Therapy High Field MR Center, Medical University of Vienna Vienna, Austria MedicalResearch.com: What is the background for this study? What are the main findings? Response: The background is the development of a new brain therapy which allows to support brain regeneration by activation of neurons with pulsed ultrasound. Main findings are that Alzheimer's patients improve their memory up to 3 months. (more…)
Abuse and Neglect, Alzheimer's - Dementia, Autism, Medical Imaging, Mental Health Research, MRI, Multiple Sclerosis, Neurology, Technology / 23.12.2019

MedicalResearch.com Interview with: Sebastian Magda, Ph.D Director of Science & Engineering CorTechs Labs, Inc MedicalResearch.com: What is the background for this study? Response: Previous studies have shown that the changes of brain structure volume and/or metabolic activity are associated with various neurological diseases. We have created an artificial intelligence clinical decision support tool based on brain volumetric and PET metabolic activity measurements as well as other clinical measurements. (more…)
Aging, Alzheimer's - Dementia, Author Interviews, MRI, Technology / 23.12.2019

MedicalResearch.com Interview with: Dr. Weidong Luo Principal Scientist CorTechs Labs  MedicalResearch.com: What is the background for this study? Response: We were interested in whether or not we can predict the age of the brain accurately from T1 weighted MRI and/or fluorodeoxyglucose (FDG) PET scans using the brain volumetric and the relative metabolic activity. The uptake of FDG is a clinical marker used to measure the uptake of glucose and therefore metabolism. Also, we were interested in the patterns of the predicted ages for Alzheimer's disease (AD) and minor cognitive impairment (MCI) subjects when using their brain measurements for age prediction in the normal brain age model.   (more…)
Author Interviews, Medical Imaging, Technology / 11.12.2019

MedicalResearch.com Interview with: Dr. David Steiner, MD PhD Google Health, USA MedicalResearch.com: What is the background for this study? Response: Advances in artificial intelligence raise promising opportunities for improved interpretation of chest X-rays and many other types of medical images. However, even before researchers begin to address the critical question of clinical validation, there is important work to be done establishing strategies for evaluating and comparing different artificial intelligence algorithms. One challenge is defining and collecting the correct clinical interpretation or “label” for the large number of chest X-rays needed to train and evaluate these algorithms. Another important challenge is evaluating the algorithm on a dataset that actually represents the diversity of the cases encountered in clinical practice. For example, it might be relatively easy to make an algorithm that performs perfectly on a few hundred or so “easy” cases, but this of course might not be particularly useful in practice. (more…)
Addiction, Author Interviews, Opiods, Technology / 10.12.2019

MedicalResearch.com Interview with: Anna Konova, PhD Assistant Professor, Dept. of Psychiatry & UBHC Core Faculty, Brain Health Institute Rutgers University - New Brunswick MedicalResearch.com: What is the background for this study? Response: Opioid reuse and relapse are common outcomes even when a person is seeking treatment for their addiction. These reuse events pose many health risks, as well as risk for treatment failure. We currently lack the much needed tools to understand and predict this reuse vulnerability. In this study, we used computer games that assess a person's decision making process, to get at psychological processes related to how people make decisions involving risks, when they transitioned between lower and higher reuse vulnerability states during the first few months of opioid treatment. (more…)
Author Interviews, Technology, Vaccine Studies / 15.11.2019

MedicalResearch.com Interview with: Sandra Crouse Quinn, PhD Professor and Chair, Department of Family Science Senior Associate Director, Maryland Center for Health Equity School of Public Health University of Maryland College Park, MDSandra Crouse Quinn, PhD Professor and Chair, Department of Family Science Senior Associate Director, Maryland Center for Health Equity School of Public Health University of Maryland College Park, MD  MedicalResearch.com: What is the background for this study? Response: Millions of Americans use Facebook (FB) for a variety of purposes, including seeking health information. However, it is difficult for FB users to discern what is credible and scientifically sound information on the platform.  Facebook ads also focus on health issues including vaccination, which is labelled by FB as an issue of national importance.  We were the first team to conduct a research study examining the FB Ad Archive and analyzed ads that contained vaccine content. (more…)
Author Interviews, JAMA, Sexual Health, STD, Technology, UCSD / 09.11.2019

MedicalResearch.com Interview with: Alicia Nobles, PhD, MS Research Fellow Department of Medicine UC San Diego  MedicalResearch.com: What is the background for this study? Response: Sexually transmitted diseases (STDs) are at record-high rates according to the Centers for Disease Control. Between STDs being highly stigmatized infections and people lacking access to health care, people may elect to turn to social media to connect with others. This is precisely why social media sites are so popular - because they do allow for people to talk with others rapidly. Reddit, a social media site that rivals Twitter with 330 million active users and is the 6th most visited website in the United States, is organized into online communities, many of which discuss health topics. We monitored all r/STD (www.reddit.com/r/STD/) posts, where users can find “anything and everything STD related,” from its inception in November 2010 through February 2019.   (more…)
Addiction, Author Interviews, Mental Health Research, Technology / 29.10.2019

MedicalResearch.com Interview with: Zsolt Demetrovics PhD and Orsolya Király PhD Department of Clinical Psychology and Addiction Institute of Psychology ELTE Eötvös Loránd University MedicalResearch.com: What is the background for this study? Response: Gaming disorder has recently been recognized by the World Health Organization (WHO) as a mental disorder. Research examining gaming motivations and mental health among video gamers and in relation with gaming disorder is increasing but different types of gamers such as recreational gamers and esport gamers are not commonly distinguished. Esport is form of electronic sport and refers to playing video games in a professional (competitive) manner in sports-like tournaments. Much like in the case of traditional sports, esport players and teams are sponsored, tournaments are broadcasted and followed by large audiences and have large financial prizes. Therefore, being an esports player in now a real career opportunity for teenagers and young adults who like playing video games.  (more…)
Author Interviews, Ophthalmology, Technology / 21.10.2019

MedicalResearch.com Interview with: Louis R. Pasquale, MD Professor of Ophthalmology Icahn School of Medicine at Mount Sinai; Site Chair of the Department of Ophthalmology at The Mount Sinai Hospital and Mount Sinai Queens; Vice Chair of Translational Ophthalmology Research Mount Sinai Health System  MedicalResearch.com: What is the background for this study? What are the main findings? Response: Individual visual field tests provide a 52-point array of functional information about a glaucoma patient but it does not give us a handle on how functionally disabled they might be. A series of visual field tests need to be assessed for functional progression but current conventional algorithms for doing so are governed by ad hoc rules and the various algorithms available for assessing progression do not agree with one another. Finally, in managed care setting where one might be responsible for allocating resources for large numbers of glaucoma patients, it would be valuable to quickly visualize which patients are progressing rapidly and which ones are stable. This could allow for proper allocation of resources and perhaps inquiry into why a subset of patients are doing poorly. We wanted to develop an easy to use tool to quickly visualize how individual glaucoma patients and how groups of glaucoma patients are doing from a functional perspective. (more…)
Author Interviews, Dermatology, JAMA, Melanoma, Technology / 21.10.2019

MedicalResearch.com Interview with: https://skin-analytics.com/about-us/ Dr. Helen Marsden PhD Skin Analytics Limited London, United Kingdom MedicalResearch.com: What is the background for this study? Response: In this technology age, with the explosion of interest and applications using Artificial Intelligence, it is easy to accept the output of a technology-based test - such as a smartphone app designed to identify skin cancer - without thinking too much about it. In reality, technology is only as good as the way it has been developed, tested and validated. In particular, AI algorithms are prone to a lack of “generalisation” - i.e. their performance drops when presented with data it has not seen before. In the medical field, and particularly in areas where AI is being developed to direct a patient’s diagnosis or care, this is particularly problematic. Inappropriate diagnosis or advice to patients can lead to false reassurance, heightened concern and pressure on NHS services, or worse. It is concerning, therefore, that there are a large number of smartphone apps available that provide an assessment of skin lesions, including some that provide an estimate of the probability of malignancy, that have not been assessed for diagnostic accuracy. Skin Analytics has developed an AI-based algorithm, named: Deep Ensemble for Recognition of Malignancy (DERM), for use as a decision support tool for healthcare providers. DERM determines the likelihood of skin cancer from dermoscopic images of skin lesions. It was developed using deep learning techniques that identify and assess features of these lesions which are associated with melanoma, using over 7,000 archived dermoscopic images. Using these images, it was shown to identify melanoma with similar accuracy to specialist physicians. However, to prove the algorithm could be used in a real life clinical setting, Skin Analytics set out to conduct a clinical validation study. (more…)
Author Interviews, Education, Heart Disease, Technology / 02.10.2019

MedicalResearch.com Interview with: Tiffany G. Munzer, MD Department of Pediatrics University of Michigan Medical School Ann Arbor  MedicalResearch.com: What is the background for this study? Response: There’s been such a rise in the prevalence of tablet devices and the recommendation for families of young children has been to engage in media together because children learn the most from screens when they’re shared with an adult. However, little is known about how toddlers and adults might behave and interact using a tablet. (more…)
Author Interviews, Dermatology, Melanoma, Technology / 19.09.2019

SkinVision   MedicalResearch.com Interview with:  Andreea Udrea, PhD Associate Professor University Politehnica of Bucharest   MedicalResearch.com: What is the background for this study? The skin cancer incidence rate is increasing worldwide. Early diagnosis and prevention can reduce morbidity and are also linked to decreased healthcare costs. During the last years, efforts have been made in developing smartphone applications for skin lesion risk assessment to be used by laypersons. In parallel, as machine learning (ML) is on the rise, and medical image databases are increasing in size, a series of algorithms have been developed and compared in clinical studies to dermatologists for skin cancer diagnosis. The accuracy of the algorithms and experts were comparable. One drawback of these clinical studies is that they use images acquired by professionals in standardized conditions. So, there is little knowledge of what the accuracy will be when including an ML algorithm in an app and testing it in a non-clinical setup where the image quality may be lower, and the variability in image taking scenarios is higher as images are acquired by non-professionals using the smartphone camera. This study is one of the first that evaluates the accuracy of an app (SkinVision) when being used for risk assessment of skin lesions in the general population. (more…)
Author Interviews, Heart Disease, Pharmaceutical Companies, Stem Cells, Technology / 12.08.2019

MedicalResearch.com Interview with: Misti Ushio, Ph.D. Chief Executive Officer Michael Graziano, PhD Chief Scientific Officer TARA Biosystem MedicalResearch.com: What is the background for this study? Response: Almost half of all drug recalls are due to cardiac toxicity that was not picked up during early screens. These human cardiac liabilities can go undetected because historically it has been challenging to predict how human hearts will respond to potentially cardiotoxic drugs despite rigorous testing in both animals and in vitro systems throughout drug development. Traditional in vitro systems and animal models do not translate well to humans, and human donor tissue availability is limited for in vitro testing. There is great potential for human-induced pluripotent stem cell cardiomyocytes (iPSC-CMs) to bridge this human translation gap, but it’s been a challenge to train these cells to recapitulate pharmacology seen in mature human heart cells. This stems from the fact that existing experimental models utilize immature human iPSC-CMs which lack relevant physiological hallmarks of adult human cardiac muscle and therefore fail to predict drug responses seen in the clinic. (more…)
AHA Journals, Author Interviews, Blood Pressure - Hypertension, Technology / 07.08.2019

MedicalResearch.com Interview with: Kang Lee, PhD Dr Eric Jackman Institute of Child Study University of Toronto Toronto, Canada MedicalResearch.com: What is the background for this study? Response: We use a technology called transdermal optical imaging I and my postdoc invented to record facial blood flow using a regular video camera on the smartphone. This technology capitalizes on the fact that light travels beneath the facial skin and reflect off the hemoglobin under the skin. Our technology captures the minute reflected photons to decode facial blood changes due to our pulses and other physiological activities. Using machine learning, a neural network model learns to use the facial blood flow to predict blood pressures taken with a FDA approved scientific blood pressure measurement instrument. We then use the final model to predict the blood pressures of a new group of participants whose data had never been used in the model training. (more…)
Author Interviews, Heart Disease, Lancet, Mayo Clinic, Technology / 02.08.2019

MedicalResearch.com Interview with: Paul Friedman, M.D. Professor of Medicine Norman Blane & Billie Jean Harty Chair Mayo Clinic Department of Cardiovascular Medicine Honoring Robert L. Frye, M.D. MedicalResearch.com: What is the background for this study? Response: Atrial fibrillation is an irregular heart rhythm that is often intermittent and asymptomatic.  It is estimated to affect 2.7–6.1 million people in the United States, and is associated with increased risk of stroke, heart failure and mortality. It is difficult to detect and often goes undiagnosed. After an unexplained stroke, it is important to accurately detect atrial fibrillation so that patients with it are given anticoagulation treatment to reduce the risk of recurring stroke, and other patients (who may be harmed by this treatment) are not. Currently, detection in this situation requires monitoring for weeks to years, sometimes with an implanted device, potentially leaving patients at risk of recurrent stroke as current methods do not always accurately detect atrial fibrillation, or take too long. We hypothesized that we could train a neural network to identify the subtle findings present in a standard 12-lead electrocardiogram (ECG) acquired during normal sinus rhythm that are due to structural changes associated with a history of (or impending) atrial fibrillation.   Such an AI enhanced ECG (AI ECG) would be inexpensive, widely available, noninvasive, performed in 10 seconds, and immensely useful following embolic stroke of unknown source to guide therapy. To test this hypothesis, we trained, validated, and tested a deep convolutional neural network using a large cohort of patients from the Mayo Clinic Digital Data Vault. (more…)