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. 
Author Interviews, Ophthalmology, Technology / 21.10.2019

MedicalResearch.com Interview with: [caption id="attachment_51888" align="alignleft" width="130"]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 Dr. Pasquale[/caption] 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.
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.
Author Interviews, Education, Heart Disease, Technology / 02.10.2019

MedicalResearch.com Interview with: [caption id="attachment_51701" align="alignleft" width="200"]Tiffany G. Munzer, MD Department of Pediatrics University of Michigan Medical School Ann Arbor Dr. Munzer[/caption] 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.
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.
Author Interviews, Heart Disease, Pharmaceutical Companies, Stem Cells, Technology / 12.08.2019

MedicalResearch.com Interview with: [caption id="attachment_50687" align="alignleft" width="125"]Misti Ushio, Ph.D. Chief Executive Office Dr. Ushio[/caption] Misti Ushio, Ph.D. Chief Executive Officer [caption id="attachment_50688" align="alignleft" width="125"]Michael Graziano, PhD Chief Scientific Officer TARA Biosystems Dr. Graziano[/caption] 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.
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.
Author Interviews, Heart Disease, Lancet, Mayo Clinic, Technology / 02.08.2019

MedicalResearch.com Interview with: [caption id="attachment_50536" align="alignleft" width="166"]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. Dr. Friedman[/caption] 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.
Addiction, Author Interviews, JAMA, Technology / 11.07.2019

MedicalResearch.com Interview with: video-gamesDr. Klaus Wölfling Psychologische Leitung - Ambulanz für Spielsucht Klinik und Poliklinik für Psychosomatische Medizin und Psychotherapie Universitätsmedizin der Johannes Gutenberg-Universität Mainz Mainz MedicalResearch.com: What is the background for this study? What are the main findings?  Response:  Our institution, the outpatient clinic of Behavioral Addictions at the Department of Psychosomatic Medicine, University Medical Center Mainz started as a pilot project, which was funded by Rhineland-Palatine, our federal state in Germany. We rapidly noticed the need for treatment in the population. We revealed insights of the disease during the last decade. During this time, we developed and refined therapeutic processes addressing Internet Addiction and Gaming Disorder. We conducted a pilot study, which tested the feasibility of a CBT-treatment approach for Internet Addiction in an RCT. We learned a lot from therapy research and noticed that it was important to conduct a study, which indicates an effective treatment for this disease. STICA found a strong remission rate for Internet and Computer game Addiction of treatment group vs. WLC (OR=10.10; 94% CI 3.69 to 27.65).
Author Interviews, Cancer Research, Pediatrics, Technology / 02.07.2019

MedicalResearch.com Interview with: atomwiseAbraham Heifets, PhD Department of Computer Science University of Toronto  MedicalResearch.com: What is the background for this announcement? How many children and adolescents are affected by pediatric cancer? Response: Cancer is diagnosed in more than 15,000 children and adolescents each year. Many cancers, including pediatric cancer, do not have effective treatments and for those that do, it is estimated that 80% have serious adverse effects that impact long-term health. 
Author Interviews, JAMA, Pediatrics, Sexual Health, Technology / 28.06.2019

MedicalResearch.com Interview with: texting, sextingCamille Mori, B.A. (hons) M.Sc. candidate Clinical Psychology Program Determinants of Child Development Lab University of Calgary  MedicalResearch.com: What is the background for this study? Response: Sexting, which is the sharing of sexual messages, images, or videos over technological devices, has recently become a cause for concern among parents, teachers, and policy makers. However, the research on sexting among youth is still in early stages, and evidence of the risks associated with sexting is inconsistent. One way to resolve discrepancies in the field is to conduct a meta-analysis, which statistically summarizes existing research. We conducted a meta-analysis in order to examine the association between sexting and sexual activity (having sex, multiple sexual partners, and lack of contraception use). The associations between sexting and mental health related variables, including delinquent behaviour, substance use, and depression/anxiety were also examined.
Author Interviews, CT Scanning, JAMA, Surgical Research, Technology / 21.06.2019

MedicalResearch.com Interview with: [caption id="attachment_49894" align="alignleft" width="200"]Christian Krautz, MD Department of Surgery, Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen Nürnberg Erlangen, Germany Dr. Krautz[/caption] Christian Krautz, MD Department of Surgery, Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen Nürnberg Erlangen, Germany  MedicalResearch.com: What is the background for this study? What are the main findings? Response: In this preclinical study that included 720 case evaluations, visualization with Cinematic Rendering allowed a more correct and faster comprehension of the surgical anatomy compared to conventional CT imaging independent from the level of surgical experience. Therefore,Cinematic Rendering is a tool that may assist HPB surgeons with preoperative preparation and intraoperative guidance through an improved interpretation of computed tomography imaging data.
Author Interviews, Psychological Science, Technology / 12.06.2019

MedicalResearch.com Interview with: [caption id="attachment_49731" align="alignleft" width="200"]Brittany I. Davidson MA Doctoral Researcher in Information Systems University of Bath Ms. Davidson[/caption] Brittany I. Davidson MA Doctoral Researcher in Information Systems University of Bath MedicalResearch.com: What is the background for this study? What are the main findings? Response: Typically, research interested in the impact of technology, or more specifically, smartphones on people and society, use surveys to measure people’s usage. Almost always, these studies claim potential harms from using smartphones, like depression, anxiety, or poorer sleep. However, these studies simply ask people about  their behaviour rather than actually measuring it. In our study, we took 10 widely used surveys to  measure screen time, which typically asks how often people use their smartphone or how problematic their usage is. We compared this to people’s objective smartphone usage from Apple Screen time (e.g., minutes spend on iPhone, number of times they picked up their phone, and the number of notifications received). We found that there is a large discrepancy between what people self-report and what they actually do with their iPhone. This is highly problematic as the sweeping statements that claim smartphones (or technology more generally) have a negative impact on mental health are not  based on solid and robust evidence at this time, which leaves much to be desired in terms of what we really know about the  impacts of technology use on people.
Author Interviews, Brain Injury, Cognitive Issues, Technology / 22.05.2019

MedicalResearch.com Interview with: [caption id="attachment_49309" align="alignleft" width="165"]Dr.. Mahncke Dr. Mahncke[/caption] Dr. Henry W. Mahncke PhD Research neuroscientist CEO of Posit Science Corporation  MedicalResearch.com: What makes this study newsworthy?  Response: Mild Traumatic Brain Injury (mTBI) is a complex condition to treat. Patients can report many symptoms (e.g., cognitive deficits, depression, anxiety, stress, fatigue, pain, sleep difficulties, disorientation, emotional issues). Prior to this study, conducted at five military and veterans’ medical centers, there has been no highly-scalable intervention to treat the cognitive deficits associated with mTBI. This study showed that a plasticity-based, computerized, brain-training app can drive statistically and clinically significant gains in overall cognitive performance. Given the number of service members and vets with persistent cognitive deficits from TBIs, that’s a big deal.
Annals Internal Medicine, Author Interviews, Technology / 22.05.2019

MedicalResearch.com Interview with: [caption id="attachment_49198" align="alignleft" width="129"]Coleman Drake, PhDAssistant Professor, Health Policy and ManagementPitt Public HealthUniversity of Pittsburgh Graduate School of Public Health Dr. Drake[/caption] Coleman Drake, PhD Assistant Professor, Health Policy and Management Pitt Public Health University of Pittsburgh Graduate School of Public Health  MedicalResearch.com: What is the background for this study? What are the main findings?  Response: Telemedicine is frequently proposed as a solution to improve access to care in rural areas where driving to the nearest physician can take up to several hours. However, there needs to be sufficient broadband infrastructure for patients to actually use telemedicine. We found that broadband infrastructure is often insufficient to support telemedicine in the most rural areas, particularly in areas where there is inadequate access to primary care physicians and psychiatrists. 
Author Interviews, Dermatology, Science, Surgical Research, Technology / 19.05.2019

MedicalResearch.com Interview with: Haishan Zeng, PhDDistinguished ScientistImaging Unit - Integrative Oncology DepartmentBC Cancer Research CentreProfessor of Dermatology, Pathology, and Physics, University of British ColumbiaVancouver, BC, Canada Haishan Zeng, PhD Distinguished Scientist Imaging Unit - Integrative Oncology Department BC Cancer Research Centre Professor of Dermatology, Pathology, and Physics, University of British Columbia Vancouver, BC, Canada  MedicalResearch.com: What is the background for this study? What are the main findings? Response: We developed a fast multiphoton microscope system that enables clinical imaging of the skin at the level of cellular resolution. With this system, we can see microstructures inside of the skin without cutting into it. We subsequently conceived the idea of directly treating the microstructures that are responsible for disease. We increased the laser power to generate intense localized heat to destroy the targeted structure. In this study, we demonstrated the feasibility of this new treatment by targeting and closing single blood vessels using our new microscope. 
Author Interviews, Cancer Research, Genetic Research, JAMA, Technology / 30.04.2019

MedicalResearch.com Interview with: [caption id="attachment_48863" align="alignleft" width="132"]Steven J.M. Jones, Professor, FRSC, FCAHSCo-Director & Head, BioinformaticsGenome Sciences CentreBritish Columbia Cancer Research CentreVancouver, British Columbia, Canada Dr. Jones[/caption] Steven J.M. Jones, Professor, FRSC, FCAHS Co-Director & Head, Bioinformatics Genome Sciences Centre British Columbia Cancer Research Centre Vancouver, British Columbia, Canada and Jasleen Grewal, BSc.Genome Sciences CentreBritish Columbia Cancer Research CentreVancouver, British Columbia, CanadaJasleen Grewal, BSc. Genome Sciences Centre British Columbia Cancer Research Centre Vancouver, British Columbia, Canada MedicalResearch.com: What is the background for this study? Response: Cancer diagnosis requires manual analysis of tissue appearance, histology, and protein expression. However, there are certain types of cancers, known as cancers of unknown primary, that are difficult to diagnose based purely on their appearance and a small set of proteins. In our precision medicine oncogenomics program, we needed an accurate approach to confirm diagnosis of biopsied samples and determine candidate tumour types for where the primary site of the cancer was uncertain.  We developed a machine learning approach, trained on the gene expression data of over 10,688 individual tumours and healthy tissues, that has been able to achieve this task with high accuracy. Genome sequencing offers a high-resolution view of the biological landscape of cancers. RNA-Seq in particular quantifies how much each gene is expressed in a given sample. In this study, we used the entire transcriptome, spanning 17,688 genes in the human genome, to train a machine learning method for cancer diagnosis. The resultant method, SCOPE, takes in the entire transcriptome and outputs an interpretable confidence score from across a set of 40 different cancer types and 26 healthy tissues. 
Author Interviews, NYU/NYMC, PTSD, Technology / 22.04.2019

MedicalResearch.com Interview with: [caption id="attachment_48742" align="alignleft" width="200"]Charles R. Marmar, MDThe Lucius N. Littauer Professor Chair of the Department of PsychiatryNYU Langone School of Medicine Dr. Marmar[/caption] Charles R. Marmar, MD The Lucius N. Littauer Professor Chair of the Department of Psychiatry NYU Langone School of Medicine MedicalResearch.com: What is the background for this study? What are the main findings?  Response: Several studies in recent years have attempted to identify biological markers that distinguish individuals with PTSD, with candidate markers including changes in brain cell networks, genetics, neurochemistry, immune functioning, and psychophysiology. Despite such advances, the use of biomarkers for diagnosing PTSD remained elusive going into the current study, and no physical marker was applied in the clinic. Our study is the first to compare speech in an age and gender matched sample of a military population with and without PTSD, in which PTSD was assessed by a clinician, and in which all patients did not have a major depressive disorder. Because measuring voice qualities in non-invasive, inexpensive and might be done over the phone, many labs have sought to design speech-based diagnostic tools 
Author Interviews, Brigham & Women's - Harvard, Cancer Research, JAMA, Radiation Therapy, Technology / 19.04.2019

MedicalResearch.com Interview with: [caption id="attachment_48660" align="alignleft" width="200"]Raymond H Mak, MDRadiation OncologyBrigham and Women's Hospital Dr. Mak[/caption] Raymond H Mak, MD Radiation Oncology Brigham and Women's Hospital MedicalResearch.com: What is the background for this study? 
  • Lung cancer remains the most common cancer, and leading cause of cancer mortality, in the world and ~40-50% of lung cancer patients will need radiation therapy as part of their care
  • The accuracy and precision of lung tumor targeting by radiation oncologists can directly impact outcomes, since this key targeting task is critical for successful therapeutic radiation delivery.
  • An incorrectly delineated tumor may lead to inadequate dose at tumor margins during radiation therapy, which in turn decreases the likelihood of tumor control.
  • Multiple studies have shown significant inter-observer variation in tumor target design, even among expert radiation oncologists
  • Expertise in targeting lung tumors for radiation therapy may not be available to under-resourced health care settings
  • Some more information on the problem of lung cancer and the radiation therapy targeting task here:https://www.youtube.com/watch?v=An-YDBjFDV8&feature=youtu.be
Author Interviews, Diabetes, JAMA, Technology / 18.04.2019

MedicalResearch.com Interview with: [caption id="attachment_48552" align="alignleft" width="150"]Associate Professor Josip CarMD, PhD, DIC, MSc, FFPH, FRCP (Edin)​Associate Professor of Health Services Outcomes Research,​Director, Health Services Outcomes Research Programme and DirectorCentre for Population Health SciencesPrincipal Investigator, Population Health & Living Laboratory Prof. Car[/caption] Associate Professor Josip Car MD, PhD, DIC, MSc, FFPH, FRCP (Edin)​ Associate Professor of Health Services Outcomes Research,​ Director, Health Services Outcomes Research Programme and Director Centre for Population Health Sciences Principal Investigator, Population Health & Living Laboratory  MedicalResearch.com: What is the background for this study? Response: In 2018, almost 8% of people with diabetes who owned a smartphone used a diabetes app to support self-management. Currently, most apps are not regulated by the US Food and Drug Administration (FDA). We downloaded and assessed 371 diabetes self-management apps, to see if they provided evidence-based decision support and patient education. 
Author Interviews, JAMA, Pediatrics, Stanford, Technology / 26.03.2019

MedicalResearch.com Interview with: [caption id="attachment_48138" align="alignleft" width="200"]Dennis P. Wall, PhDAssociate ProfessorDepartments of Pediatrics, Psychiatry (by courtesy) and Biomedical Data ScienceStanford University Dr. Wall[/caption] Dennis P. Wall, PhD Associate Professor Departments of Pediatrics, Psychiatry (by courtesy) and Biomedical Data Science Stanford University  MedicalResearch.com: What did we already know about the potential for apps and wearables to help kids with autism improve their social skills, and how do the current study findings add to our understanding? What’s new/surprising here and why does it matter for children and families?  Response: We have clinically tested apps/AI for diagnosis (e.g.  https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002705) in a number of studies. This RCT is a third phase of a phased approach to establish feasibility and engagement through in-lab and at-home codesign with families with children with autism. This stepwise process is quite important to bring a wearable form of therapy running AI into the homes in a way that is clinically effective. What’s new here, aside from being a first in the field, is the rigorous statistical approach we take with an intent-to-treat style of analysis. This approach ensures that the effect of the changes are adjusted to ensure that any significance observed is due to the treatment.  Thus, with this, it is surprising and encouraging to see an effect on the VABS socialization sub-scale. This supports the hypothesis that the intervention has a true treatment effect and increases the social acuity of the child. With it being a home format for intervention that can operate with or without a clinical practitioner, it increases options and can help bridge gaps in access to care, such as when on waiting lists or if the care process is inconsistent.  
Accidents & Violence, Author Interviews, Technology / 26.03.2019

MedicalResearch.com Interview with: [caption id="attachment_48127" align="alignleft" width="150"]Cynthia Lum, PhDProfessor of CriminologyLaw and SocietyGeorge Mason University Dr. Lum[/caption] Cynthia Lum, PhD Professor of Criminology Law and Society George Mason University MedicalResearch.com: What is the background for this study? Response: Body-worn cameras (BWCs) are one of the most rapidly diffusing technologies in policing today, costing agencies and their municipalities millions of dollars. Recent estimates by the Bureau of Justice statistics indicate that over 60% of local police departments have already acquired BWCs. This adoption has been propelled by highly publicized officer-involved shootings and other death-in-custody events in this decade, as well as more generally by continuing concerns regarding police-citizen relationships, particularly within communities of color. All of these contexts prompt the need to better understand the impacts and effects of BWCs as they diffuse rapidly into policing. Specifically, do BWCs achieve the expectations that citizens, communities, and the police have of them? This article provides a narrative review of 70 studies, representing over 110 findings, about what we know from research across six important Body-worn cameras domains: (1) the impact of BWCs on officer behavior; (2) officer attitudes about BWCs; (3) the impact of BWCs on citizen behavior; (4) citizen and community attitudes about BWCs; (5) the impact of BWCs on criminal investigations; and (6) the impact of BWCs on law enforcement organizations.
Author Interviews, Heart Disease, JACC, Technology / 20.03.2019

MedicalResearch.com Interview with: [caption id="attachment_48004" align="alignleft" width="130"]Annapoorna Kini, MDZena and Michael A Wiener Professor of MedicineDirector of the Cardiac Catheterization LaboratoryMount Sinai Heart at Mount Sinai Hospital Dr. Kini[/caption] Annapoorna Kini, MD Zena and Michael A Wiener Professor of Medicine Director of the Cardiac Catheterization Laboratory Mount Sinai Heart at Mount Sinai Hospital MedicalResearch.com: What is the background for this study?  
  • Expanding indication and use of Transcatheter aortic valve replacement (TAVR) poses a unique problem of coronary access after valve implantation.
  • Troubleshooting tools and techniques have been published but are not available at the fingertips of the user at all the times.
  • We tried to address this unique problem with an innovative educational mobile application (app) called "TAVRcathAID".
Author Interviews, Diabetes, Technology, UCSF / 12.03.2019

MedicalResearch.com Interview with: [caption id="attachment_47913" align="alignleft" width="200"]Robert Avram MD MScDivision of CardiologyUniversity of California, San Francisco Dr. Robert Avram[/caption] Robert Avram MD MSc Division of Cardiology University of California, San Francisco MedicalResearch.com: What is the background for this study? Would you briefly describe what is meant by Photoplethysmography? While analyzing the heart rate data as collected using smartphones apps in the Health-eHeart study, we noticed that diabetic patients had, on average, a higher ‘free-living’ heart rate than non-diabetic patients when adjusted from multiple factors. This pushed us to analyze the signal to see if there were other features that would help differentiate diabetes patients from non-diabetes patients. By identifying these features, we saw a huge opportunity to develop a screening tool for diabetes using deep learning and a smartphone camera and flash, in order to classify patients as having prevalent diabetes/no-diabetes. Photoplethysmography is the technique of measuring the difference in light absorption by the skin in order to detect blood volume changes in the microvasculature. Most modern mobile devices, including smartphones and many fitness trackers (Apple Wathc, FitBit), have the ability to acquire PPG waveforms, providing a unique opportunity to detect diabetes-related vascular changes at population-scale. 
Author Interviews, Lung Cancer, Nature, Technology / 05.03.2019

MedicalResearch.com Interview with: [caption id="attachment_47795" align="alignleft" width="200"]Saeed Hassanpour, PhDAssistant ProfessorDepartments of Biomedical Data Science,Computer Science, and EpidemiologyGeisel School of Medicine at DartmouthLebanon, NH 03756 Dr. Hassanpour[/caption] Saeed Hassanpour, PhD Assistant Professor Departments of Biomedical Data Science, Computer Science, and Epidemiology Geisel School of Medicine at Dartmouth Lebanon, NH 03756 MedicalResearch.com: What is the background for this study? What are the main findings? Response: Lung cancer is the deadliest cancer for both men and women in the western world. The most common form, lung adenocarcinoma, requires pathologist’s visual examination of resection slides to determine grade and treatment. However, this is a hard and tedious task. Using new technologies in artificial intelligence and deep learning, we trained a deep neural network to classify lung adenocarcinoma subtypes on histopathology slides and found that it performed on par with three practicing pathologists.
Author Interviews, MRI, Prostate Cancer, Technology / 12.02.2019

MedicalResearch.com Interview with: [caption id="attachment_47464" align="alignleft" width="130"]Gaurav Pandey, Ph.D. Assistant Professor Department of Genetics and Genomic Sciences Icahn Institute of Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai, New York  Dr. Pandey[/caption] Gaurav Pandey, Ph.D. Assistant Professor Department of Genetics and Genomic Sciences Icahn Institute of Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai, New York  MedicalResearch.com: What is the background for this study?  Response: Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), most routinely through PI-RADS v2, but its interpretation is generally variable due to its relatively subjective nature. Radiomics, a methodology that can analyze a large number of features of images that are difficult to study solely by visual assessment, combined with machine learning methods have shown potential for improving the accuracy and objectivity of mpMRI-based prostate cancer assessment. However, previous studies in this direction are generally limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and machine learning methods.
Author Interviews, Orthopedics, Technology / 02.02.2019

MedicalResearch.com Interview with: [caption id="attachment_47270" align="alignleft" width="160"]Professor Sherry Towfighian PhD Mechanical Engineering Binghamton University   Prof. Towfighian[/caption] Professor Sherry Towfighian PhD Mechanical Engineering Binghamton University   MedicalResearch.com: What is the background for this study? What are the main findings?  Response: We wanted to avoid using batteries in a load monitor that can be placed in total knee replacement. We looked into energy scavenging technologies and studied the most appropriate one for this application. Energy scavenging is converting wasted energy such as walking to electricity for low power sensors. Our research study showed walking can provide enough electrical energy (about 6 microwatts) for low power load sensors. These load sensors are important in providing information about the mechanical load throughout different activities. It can be used in the future to create a self -awareness device for the patient to avoid certain activities. 
Author Interviews, Hospital Acquired, Infections, Technology / 23.01.2019

MedicalResearch.com Interview with: [caption id="attachment_47120" align="alignleft" width="142"]Donna Armellino RN, DNP, CIC Vice President, Infection Prevention Northwell Health, Infection Prevention Lake Success, N. Y. Dr. Armellino[/caption] Donna Armellino RN, DNP, CIC Vice President, Infection Prevention Northwell Health, Infection Prevention Lake Success, N. Y. MedicalResearch.com: What is the background for this study?  Response: The background for initiating this study was to assess frequently used equipment within the patient care environment following standard manual cleaning and disinfection compared to disinfection with PurpleSun’s shadowless 90-second cycle focused multivector ultraviolet (FMUV) delivery system. Microbes exist within the environment. Cleaning followed by disinfection, regardless of method, is intended to decrease levels of these microbes to minimize exposure and the risk of infection. To measure the effectiveness of the two methods of disinfection a five-point culturing method was used to assess microbial burden. This method was used to assess patient care equipment cleanliness after manual cleaning/disinfection and following the use of FMUV after an operative case and was used to sample equipment deemed cleaned/disinfected and ready for use outside the operative environment. Microbial burden was reported as colony forming units (CFUs). Comparison of the CFUs before cleaning/disinfection, after cleaning/disinfection, and after the use of FMUV allowed efficacy of the disinfection methods to be compared. 
Author Interviews, Dermatology, JAMA, Technology / 01.12.2018

MedicalResearch.com Interview with: Philipp Tschandl, MD PhD, Priv.Doz. Assistant Professor Department of Dermatology Medical University of Vienna MedicalResearch.com: What is the background for this study? What are the main findings? Response: Dermatoscopy is a non-invasive imaging technique, where the surface of the skin is rendered translucent and additional important morphologic features become visible from deeper layers. This is achieved through use of immersion fluid or cross-polarised light - equivalent to the effect when using a pair of goggles to look underwater, or polarised sunglasses to reduce glare on glass surfaces. After the first description of “Dermatoskopie" almost 100 years ago by a German dermatologist (Johann Saphier), this technique has evolved to a successful, low-cost, state-of-the-art technique for clinical skin cancer detection in the last decades. Convolutional neural networks (“CNN”) are powerful machine learning methods, and frequently applied to medical image data in the recent scientific literature. They are highly accurate for basic image classification tasks in experimental settings, and found to be as good as dermatologists in melanoma recognition on clinical or dermatoscopic images. In this study we trained a CNN to diagnose non-pigmented skin lesions (where melanomas are only a minority) through analysis of digital images, and compared the accuracy to >90 human readers including 62 board-certified dermatologists. This study expands knowledge in the following ways compared to previous work: - We applied the network for the detection of non-pigmented skin cancer, which is far more common in the (caucasian) population than melanoma. - We created a prediction model that combines analysis of a dermatoscopic and clinical image (“cCNN”) which is able to further increase diagnostic accuracy. - We compared accuracy not only to experts, but users with different level of experience
Author Interviews, Heart Disease, JACC, Surgical Research, Technology / 20.11.2018

MedicalResearch.com Interview with: Evolut TAVR PlatformDr. Shazia Afzal MD University Hospital Düsseldorf, Medical Faculty, Division of Cardiology, Pulmonology and Vascular Medicine, Düsseldorf, Germany MedicalResearch.com: What is the background for this study? Response: Since its introduction in 2002, transcatheter aortic valve replacement (TAVR) emerged to an increasingly important interventional procedure in the field of structural heart disease. Widespread use in Europe, the USA and Canada lead to continuous technological development and improved patient’s safety, procedural success and clinical outcome. In 08/2017 one of the market leaders introduced its latest generation valve model -the CoreValve Evolut PROTM- which was especially designed to mitigate paravalvular leakage after valve deployment. We conducted the first prospective study which directly compares the Evolut PROTM with its direct predecessor the Evolut RTM as a head-to-head analysis especially focusing on hemodynamic performance and clinical outcome in a real-world setting. To ensure comparability between groups, we performed propensity score matching with special interest in CT-derived data to guarantee equitable anatomical conditions. Since both valves are on the market but sold at different prices the pivotal question is whether the Evolut PROTM reaches its target. In a highly budget restricted health care system with limited refunding cost-effectiveness evolves to a substantial discussion point in daily clinical practice. Our results may not be marketing friendly but we think of relevance for the interventional community.