Author Interviews, Leukemia, Stem Cells, Technology / 11.02.2021

MedicalResearch.com Interview with: [caption id="attachment_56645" align="alignleft" width="130"]Eirini Papapetrou, MD, PhD Associate Professor Department of Oncological Sciences Icahn School of Medicine at Mount Sinai New York, NY 10029 Dr. Papapetrou[/caption] Eirini Papapetrou, MD, PhD Associate Professor Department of Oncological Sciences Icahn School of Medicine at Mount Sinai New York, NY 10029 MedicalResearch.com: What is the background for this study? Would you tell us a little about acute myeloid leukemia? Response: Acute myeloid leukemia is a form of cancer of the blood. It is typically very aggressive and lethal without treatment. The main treatment is high-dose chemotherapy and it has not changed very much in decades. Some more recent "targeted" therapies that are less toxic help somewhat but still do not result in cures. We believe a reason for this might be that both chemotherapy and newer "targeted" therapies target the cells at the later stages of the disease and spare the earlier ones, which can then give rise to disease resistance and relapse. 
Author Interviews, COVID -19 Coronavirus, JAMA, Race/Ethnic Diversity, Social Issues, Technology, University of Pennsylvania / 30.12.2020

MedicalResearch.com Interview with: Srinath Adusumalli, MD, MSc, FACC Assistant Professor of Clinical Medicine Division of Cardiovascular Medicine| Penn Medicine Lauren A. Eberly, MD, MPH Division of Cardiovascular Medicine, Department of Medicine Hospital of the University of Pennsylvania, Philadelphia MedicalResearch.com: What is the background for this study?   Response: The coronavirus disease 2019 (COVID-19) pandemic has uprooted conventional health care delivery for routine ambulatory care, requiring health systems to rapidly adopt telemedicine capabilities. At Penn Medicine, we wanted to ensure that as we developed a new system of telemedical care, we were reaching all of the patients we serve and access to care was maintained. As such, we undertook this study to examine utilization of care as we continued to iterate on and develop our telemedical system of care.
Author Interviews, Cancer Research, JAMA, Prostate Cancer, Technology / 13.11.2020

MedicalResearch.com Interview with: [caption id="attachment_55940" align="alignleft" width="200"]Dave Steiner MD PhD Clinical Research Scientist Google Health, Palo Alto, California Dr. Steiner[/caption] Dave Steiner MD PhD Clinical Research Scientist Google Health, Palo Alto, California MedicalResearch.com: What is the background for this study? Response: For prostate cancer patients, the grading of cancer in prostate biopsies by pathologists is central to risk stratification and treatment decisions. However, the grading process can be subjective, often resulting in variability among pathologists. This variability can complicate diagnostic and treatment decisions. As an initial step towards addressing this problem, we and others in the field have recently developed artificial intelligence (AI) algorithms that perform on-par with expert pathologists for prostate cancer grading. Such algorithms have the potential to improve the quality and efficiency of prostate biopsy grading, but the impact of these algorithms when used by pathologists has not been well studied. In the current study, we developed and evaluated an AI-based assistant tool for use by pathologists while reviewing prostate biopsies.
Author Interviews, Fertility, Genetic Research, OBGYNE, Technology / 29.10.2020

MedicalResearch.com Interview with: PGT-A & ARTIFICIAL INTELLIGENCE IMPROVES PREGNANCY OUTCOMES FOR PATIENTS UNDERGOING IVF MedicalResearch.com Interview with: Michael Large, PhD Senior Director, Research at CooperGenomics CooperSurgical MedicalResearch.com: What is the background for this study? What are the main findings? Dr. Large: Independent study results, presented at the recent the American Society of Reproductive Medicine (ASRM) Virtual Scientific Congress, demonstrated a 13 percent relative increase in ongoing pregnancy and live birth rates associated with the use of CooperSurgical’s PGTaiSM 2.0 technology to screen embryos for in vitro fertilization (IVF). The single-center study was conducted by NYU Langone Fertility Center (NYULFC), part of The Prelude Network. Preimplantation Genetic Testing for aneuploidy (PGT-A) is performed on embryos produced through IVF; it provides genetic information to help identify embryos that are more likely to result in a successful pregnancy. PGTai 2.0 technology is an advancement in PGT-A testing platform that utilizes artificial intelligence to increase objectivity of this screening process. The study compared results from three next generation sequencing (NGS) genetic tests: Standard NGS, NGS with first generation artificial intelligence (PGTai 1.0 Technology Platform) and NGS with second generation artificial intelligence (PGTai 2.0 Technology Platform). The ongoing pregnancy and live birth rates significantly increased by a relative 13 percent in the PGTai 2.0 group as compared to subjective and prior methodologies. Study results also suggest that the increase in ongoing pregnancy and live births may be linked to improvements in several preceding IVF outcomes (implantation rates, clinical pregnancy rates and pregnancy loss.) MedicalResearch.com: What should readers take away from your report? Dr. Large: This research moves us an important step closer to our goal of increased live births, improved pregnancy outcomes and further reduction of multiples in pregnancy through greater confidence in single embryo transfer. An estimated 48.5 million couples – approximately 15% of couples -- are affected by infertility worldwide. 80,000 babies were born with IVF in 2017 in the United States and more than one million babies were born in the period 1987 to 2015 in the United States as a result of IVF. MedicalResearch.com: What recommendations do you have for future research this study? Dr. Large: The goal of PGT-A is to decrease risk and maximize the chances of IFV success by screening for embryos with the highest potential. This was precisely what NYULFC have observed so far with PGTai 2.0 compared to older technologies. To fully appreciate the impact that these improvements are having for patients, we’re excited to hear from additional IVF centers across the world as they utilize this technology. MedicalResearch.com: Is there anything else you would like to add? Any disclosures? Dr. Large: The study demonstrates CooperSurgical’s commitment to developing the most advanced technology in the field of genetic testing to advance reproductive medicine and help families. By applying artificial intelligence in the PGTaism2.0 technology, we leverage mathematical algorithms derived from real-world data to achieve objective embryo assessment. I am the Senior Director of Genomics Research and Development at CooperSurgical. Michael Large, PhD, is the Senior Director, Genomics Research and Development at CooperSurgical. His team recently led and continues to develop state-of-the-art analytical methods for interrogating Reproductive Genetics. Dr. Large earned his PhD in Cell and Molecular Biology from the Baylor College of Medicine and his Bachelor of Science in Cell and Molecular Biology from the University of Wisconsin – La Crosse. Michael Large, PhD Senior Director, Research at CooperGenomics CooperSurgical   MedicalResearch.com: What is the background for this study? What are the main findings? Dr. Large: Independent study results, presented at the recent the American Society of Reproductive Medicine (ASRM) Virtual Scientific Congress, demonstrated a 13 percent relative increase in ongoing pregnancy and live birth rates associated with the use of CooperSurgical’s PGTaiSM 2.0 technology to screen embryos for in vitro fertilization (IVF).[1] The single-center study was conducted by NYU Langone Fertility Center (NYULFC), part of The Prelude Network. Preimplantation Genetic Testing for aneuploidy (PGT-A) is performed on embryos produced through IVF; it provides genetic information to help identify embryos that are more likely to result in a successful pregnancy. PGTai 2.0 technology is an advancement in PGT-A testing platform that utilizes artificial intelligence to increase objectivity of this screening process. The study compared results from three next generation sequencing (NGS) genetic tests: Standard NGS, NGS with first generation artificial intelligence (PGTai 1.0 Technology Platform) and NGS with second generation artificial intelligence (PGTai 2.0 Technology Platform). The ongoing pregnancy and live birth rates significantly increased by a relative 13 percent in the PGTai 2.0 group as compared to subjective and prior methodologies. Study results also suggest that the increase in ongoing pregnancy and live births may be linked to improvements in several preceding IVF outcomes (implantation rates, clinical pregnancy rates and pregnancy loss.)
Author Interviews, COVID -19 Coronavirus, Health Care Systems, JAMA, Technology / 27.10.2020

MedicalResearch.com Interview with: [caption id="attachment_55764" align="alignleft" width="129"]Shira H. Fischer, MD, PhD RAND Corporation Boston, Massachusetts Dr. Fischer[/caption] Shira H. Fischer, MD, PhD RAND Corporation Boston, Massachusetts MedicalResearch.com: What is the background for this study? What are the main findings? Response: Before the COVID-19 outbreak, telehealth was talked about a lot, but it wasn’t widely available and wasn’t used that often. We wanted to know who was using telehealth, what the barriers to use were, and whether people would be willing to do so if it were available to them. We conducted a survey of over 2,500 Americans across the country and asked them about these topics. 
Author Interviews, Nutrition, NYU/NYMC, Pediatrics, Pediatrics, Technology / 26.10.2020

MedicalResearch.com Interview with: [caption id="attachment_55746" align="alignleft" width="200"]Marie Bragg, PhD Assistant Professor, Department of Population Health on Health Choice NYU College of Global Public Health Dr. Bragg[/caption] Marie Bragg, PhD Assistant Professor, Department of Population Health on Health Choice NYU College of Global Public Health MedicalResearch.com: What is the background for this study? Response: We know from previous research that children who see food advertisements eat significantly more calories than children who see non-food advertisements. Those studies led the World Health Organization and National Academy of Medicine to issue reports declaring that exposure to food advertising is a major driver of childhood obesity. What we don’t know is how frequently unhealthy food and beverage brands are appearing in YouTube videos posted by Kid Influencers. Kid influences are children whose parents film videos of the child playing with toys, unwrapping presents, eating food, or engaging in other family-friendly activities. The parents then post the videos to YouTube for other children and parents to view for entertainment. 
Author Interviews, Emergency Care, Social Issues, Technology / 23.10.2020

MedicalResearch.com Interview with: [caption id="attachment_55726" align="alignleft" width="133"]Susan Lu PhD Gerald Lyles Rising Star Associate Professor of Management Krannert School of Management Purdue University Dr. Love you to the moon and back![/caption] Susan Lu PhD Gerald Lyles Rising Star Associate Professor of Management Krannert School of Management Purdue University  MedicalResearch.com: What is the background for this study? Response: We started this project in 2016. Overcrowding in emergency rooms (ERs) is a common yet nagging problem. It not only is costly for hospitals but also compromises care quality and patient experience. Hence, finding effective ways to improve ER care delivery is of great importance. Meanwhile, the advancement of healthcare technologies including electronic medical records, online doctor ratings and 4G mobile network motivates us to think about the impact of telemedicine on ER operations in the near future. 
Author Interviews, Brigham & Women's - Harvard, Fertility, Technology / 16.09.2020

MedicalResearch.com Interview with: [caption id="attachment_55400" align="alignleft" width="200"]Hadi Shafiee, PhD Assistant Professor, Harvard Medical School Brigham and Women's Hospital Department of Medicine Dr. Shafiee[/caption] 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.
Author Interviews, Brigham & Women's - Harvard, Gastrointestinal Disease, Technology / 27.08.2020

MedicalResearch.com Interview with: [caption id="attachment_29758" align="alignleft" width="143"]C. Giovanni Traverso, MB, BChir, PhD Gastroenterologist and biomedical engineer Division of Gastroenterology at BWH Instructor of medicine at Harvard Medical School. Dr. Traverso[/caption] 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.
Author Interviews, Cancer Research, Nature, Technology / 06.08.2020

MedicalResearch.com Interview with: [caption id="attachment_55045" align="alignleft" width="200"]Moritz Gerstung PhD Group Leader: Computational cancer biology EMBL-European Bioinformatics Institute Dr. Gerstung[/caption] 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. 
Author Interviews, Genetic Research, Nature, Technology / 15.07.2020

MedicalResearch.com Interview with: [caption id="attachment_54863" align="alignleft" width="200"]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 Dr. Akalin[/caption] 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. 
Author Interviews, Dermatology, Melanoma, Nature, Technology / 23.06.2020

MedicalResearch.com Interview with: [caption id="attachment_54678" align="alignleft" width="127"]Professor Harald Kittler, MD ViDIR Group, Department of Dermatology Medical University of Vienna Vienna, Austria Prof. Kittler[/caption] 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.
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.
Author Interviews, Dermatology, Melanoma, Technology / 17.06.2020

MedicalResearch.com Interview with: [caption id="attachment_54605" align="alignleft" width="144"]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 Dr. Chi Hwan Lee[/caption] 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).
Author Interviews, JAMA, Pediatrics, Social Issues, Technology / 18.05.2020

MedicalResearch.com Interview with: [caption id="attachment_54246" align="alignleft" width="200"]Pooja S. Tandon, MD, MPH Center for Child Health, Behavior and Development Seattle Children's Research Institute Dr. Tandon[/caption] 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.
Author Interviews, Dermatology, Technology / 03.04.2020

MedicalResearch.com Interview with: [caption id="attachment_53761" align="alignleft" width="168"]Jung-Im Na, MD PhD Associate Professor, Department of Dermatology Seoul National University Bundang Hospital  Korea Dr. Jung Im Na[/caption] 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.
Author Interviews, Columbia, COVID -19 Coronavirus, NYU/NYMC, 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.
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. 
Author Interviews, BMJ, Dermatology, Technology / 13.02.2020

MedicalResearch.com Interview with: [caption id="attachment_53101" align="alignleft" width="200"]Professor Jon Deeks PhD, CStat Institute of Applied Health Research Professor of Biostatistics College of Medical and Dental Sciences University of Birmingham, UK Prof. Deeks[/caption] 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.
Addiction, Author Interviews, Technology / 29.01.2020

MedicalResearch.com Interview with: [caption id="attachment_51918" align="alignleft" width="158"]John W. Ayers, PhD, MA Vice Chief of Innovation | Assoc. Professor Div. Infectious Disease & Global Public Health University of California San Diego Dr. Ayers[/caption] 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.
Alzheimer's - Dementia, Author Interviews, Cognitive Issues, McGill, Neurology, Technology / 28.01.2020

MedicalResearch.com Interview with: [caption id="attachment_52997" align="alignleft" width="160"]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 Dr. Iturria-Medina[/caption] 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.
Author Interviews, JAMA, Surgical Research, Technology, University of Michigan / 13.01.2020

MedicalResearch.com Interview with: [caption id="attachment_52750" align="alignleft" width="144"]Kyle Sheetz, MD Clinical Year 4 Resident, General Surgery Michigan Medicine Dr. Sheetz[/caption] 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.
Alzheimer's - Dementia, Author Interviews, Technology / 08.01.2020

MedicalResearch.com Interview with: [caption id="attachment_52700" align="alignleft" width="200"]Ao.Univ.-Prof. Priv.-Doz. 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 Dr. Beisteiner[/caption] 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.
Abuse and Neglect, Alzheimer's - Dementia, Autism, Medical Imaging, Mental Health Research, MRI, Multiple Sclerosis, Neurology, Technology / 23.12.2019

MedicalResearch.com Interview with: [caption id="attachment_52575" align="alignleft" width="156"]Sebastian Magda, Ph.D Director of Science & Engineering CorTechs Labs, Inc Dr. Sebastian Magda[/caption] 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.
Aging, Alzheimer's - Dementia, Author Interviews, MRI, Technology / 23.12.2019

MedicalResearch.com Interview with: [caption id="attachment_52568" align="alignleft" width="200"]Dr. Weidong Luo Principal Scientist CorTechs Labs Dr. Weidong Luo[/caption] 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.  
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.
Addiction, Author Interviews, Opiods, Technology / 10.12.2019

MedicalResearch.com Interview with: [caption id="attachment_52410" align="alignleft" width="148"]Anna Konova, PhD Assistant Professor, Dept. of Psychiatry & UBHC Core Faculty, Brain Health Institute Rutgers University - New Brunswick Dr. Anna Konova[/caption] 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.
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.
Author Interviews, JAMA, Sexual Health, STD, Technology, UCSD / 09.11.2019

MedicalResearch.com Interview with: [caption id="attachment_52020" align="alignleft" width="200"]Alicia Nobles, PhD, MS Research Fellow Department of Medicine UC San Diego Dr. Nobles[/caption] 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.