Author Interviews, Brigham & Women's - Harvard, Cancer Research, JAMA, Lancet, Lung Cancer, Medical Imaging, Technology / 07.09.2022

MedicalResearch.com Interview with: Raymond H. Mak, MD Radiation Oncology Disease Center Leader for Thoracic Oncology Director of Patient Safety and QualityDirector of Clinical Innovation Associate Professor, Harvard Medical School Cancer - Radiation OncologyRadiation Oncology Department of Radiation Oncology Brigham and Women's Hospital MedicalResearch.com: What is the background for this study? What is the algorithm detecting? Response: Lung cancer, the most common cancer worldwide is highly lethal, but can be treated and cured in some cases with radiation therapy.  Nearly half of lung cancer patients will eventually require some form of radiation therapy, but the planning for a course of radiation therapy currently entails manual, time-consuming, and resource-intensive work by highly trained physicians to segment (target) the cancerous tumors in the lungs and adjacent lymph nodes on three-dimensional images (CT scans). Prior studies have shown substantial variation in how expert clinicians delineate these targets, which can negatively impact outcomes and there is a projected shortage of skilled medical staff to perform these tasks worldwide as cancer rates increase. To address this critical gap, our team developed deep learning algorithms that can automatically target lung cancer in the lungs and adjacent lymph nodes from CT scans that are used for radiation therapy planning, and can be deployed in seconds. We trained these artificial intelligence (AI) algorithms using expert-segmented targets from over 700 cases and validated the performance in over 1300 patients in external datasets (including publicly available data from a national trial), benchmarked its performance against expert clinicians, and then further validated the clinical usefulness of the algorithm in human-AI collaboration experiments that measured accuracy, task speed, and end-user satisfaction. (more…)
Author Interviews, Education, JAMA, Surgical Research, Technology / 22.03.2022

MedicalResearch.com Interview with: Ali M. Fazlollahi, MSc, McGill Medicine Class of 2025 Neurosurgical Simulation and Artificial Intelligence Learning Centre Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital Faculty of Medicine and Health Sciences McGill University, Montreal, Canada MedicalResearch.com:  What is the background for this study?  Response: COVID-19 disrupted hands on surgical exposure of medical students and academic centres around the world had to quickly adapt to teaching technical skills remotely. At the same time, advances in artificial intelligence (AI) allowed researchers at the Neurosurgical Simulation and Artificial Intelligence Learning Centre to develop an intelligent tutoring system that evaluates performance and provides high-quality personalized feedback to students. Because this is the first AI system capable of providing surgical instructions in simulation, we sought to evaluate its effectiveness compared with learning from expert human instructors who provided coaching remotely. (more…)
Alzheimer's - Dementia, Author Interviews, JAMA, Ophthalmology / 06.12.2021

MedicalResearch.com Interview with: Cecilia S. Lee, MD, MS Associate Professor,Director, Clinical Research Department of Ophthalmology Harborview Medical Center University of Washington Seattle, WA MedicalResearch.com: What is the background for this study? Response: Cataract is a natural aging process of the eye and affects the majority of older adults who are at risk for dementia. Sensory loss, including vision and hearing, is of interest to the research community as a possible risk factor for dementia, and also as a potential point of intervention. Because cataract surgery improves visual function, we hypothesized that older people who undergo cataract surgery may have a decreased risk of developing Alzheimer disease and dementia. We used the longitudinal data from an ongoing, prospective, community based cohort, Adult Changes in Thought (ACT) study. The ACT study includes over 5000 participants to date who are dementia free at recruitment and followed until they develop Alzheimer disease or dementia. We had access to their extensive medical history including comprehensive ophthalmology visit data. We investigated whether cataract surgery was associated with a decreased risk of developing Alzheimer disease and dementia.  (more…)
Author Interviews, Cancer Research, Dermatology, Lancet, Melanoma, Technology / 11.11.2021

MedicalResearch.com Interview with: Dr David Wen BM BCh NIHR Academic Clinical Fellow in Dermatology University of Oxford MedicalResearch.com: What is the background for this study? Response: Publicly available skin image datasets are commonly used to develop machine learning (ML) algorithms for skin cancer diagnosis. These datasets are often utilised as they circumvent many of the barriers associated with large scale skin lesion image acquisition. Furthermore, publicly available datasets can be used as a benchmark for direct comparison of algorithm performance. Dataset and image metadata provide information about the disease and population upon which the algorithm was trained or validated on. This is important to know because machine learning algorithms heavily depend on the data used to train them; algorithms used for skin lesion classification frequently underperform when tested on independent datasets to which they were trained on. Detailing dataset composition is essential for extrapolating assumptions of generalisability of algorithm performance to other populations. At the time this review was conducted, the total number of publicly available datasets globally and their respective content had not previously been characterised. Therefore, we aimed to identify publicly available skin image datasets used to develop ML algorithms for skin cancer diagnosis, to categorise their data access requirements, and to systematically evaluate their characteristics including associated metadata.   (more…)
Author Interviews, Dermatology, JAMA, Technology / 28.04.2021

MedicalResearch.com Interview with: Yun Liu, PhD Google Health Palo Alto, California MedicalResearch.com: What is the background for this study? Would you describe the system?  Does it use dermatoscopic images? Response: Dermatologic conditions are extremely common and a leading cause of morbidity worldwide. Due to limited access to dermatologists, patients often first seek help from non-specialists. However, non-specialists have been reported to have lower diagnostic accuracies compared to dermatologists, which may impact the quality of care. In this study, we built upon prior work published in Nature Medicine, where we developed a computer algorithm (a deep learning system, DLS) to interpret de-identified clinical images of skin conditions and associated medical history (such as whether the patient reported a history of psoriasis). These clinical images are taken using consumer-grade hardware such as point-and-shoot cameras and tablets, which we felt was a more accessible and widely-available device compared to dermatoscopes. Given such images of the skin condition as input, the DLS outputs a differential diagnosis, which is a rank-ordered list of potential matching skin conditions. In this paper, we worked with user experience researchers to create an artificial intelligence (AI) tool based on this DLS. The tool was designed to provide clinicians with additional information per skin condition prediction, such as textual descriptions, similar-appearing conditions, and the typical clinical workup for the condition. We then conducted a randomized study where 40 clinicians (20 primary care physicians, 20 nurse practitioners) reviewed over 1,000 cases -- with half the cases with the AI-based assistive tool, and half the cases without. For each case, the reference diagnosis was based on a panel of 3 dermatologists.  (more…)
AHA Journals, Author Interviews, Heart Disease, Stroke / 12.03.2021

MedicalResearch.com Interview with: Brandon K Fornwalt, MD, PhD Associate Professor, Director Department of Imaging Science and Innovation Geisinger MedicalResearch.com: What is the background for this study? Response: Atrial fibrillation (AF) is an abnormal heart rhythm that is associated with outcomes such as stroke, heart failure and death. If we know a patient has atrial fibrillation, we can treat them to reduce the risk of stroke by nearly two-thirds. Unfortunately, patients often don’t know they have AF. They present initially with a stroke, and we have no chance to treat them before this happens. If we could predict who is at high risk of either currently having AF or developing it in the near future, we could intervene earlier and hopefully reduce bad outcomes like stroke. Artificial intelligence approaches may be able to help with this task. (more…)
Author Interviews, Brigham & Women's - Harvard, Cancer Research, Genetic Research, Melanoma, Prostate Cancer / 23.11.2020

MedicalResearch.com Interview with: Saud H AlDubayan, M.D. Instructor in Medicine, Harvard Medical School Attending Physician, Division of Genetics, Brigham and Women's Hospital Computational Biologist, Department of Medical Oncology, Dana-Farber Cancer Institute Associate Scientist, The Broad Institute of MIT and Harvard  MedicalResearch.com: What is the background for this study? What are the main findings? Response: The overall goal of this study was to assess the performance of the standard method currently used to detect germline (inhered) genetic variants in cancer patients and whether we could use recent advances in machine learning techniques to further improve the detection rate of clinically relevant genetic alterations. To investigate this possibility, we performed a head to head comparison between the current gold-standard method for germline analysis that has been universally used in clinical and research laboratories and a new deep learning analysis approach using germline genetic data of thousands of patients with prostate cancer or melanoma. This analysis showed that across all different gene sets that were tested, the deep learning-based framework was able to identify additional cancer patients with clinically relevant germline variants that went undetected by the standard method. For example, several patients in our study also had germline variants that are associated with an increased risk of ovarian cancer, for which the surgical removal of the ovaries (at a certain age) is highly recommended. However, these genetic alterations were only identified by the proposed deep learning framework.     (more…)
Author Interviews, Cancer Research, JAMA, Prostate Cancer, Technology / 13.11.2020

MedicalResearch.com Interview with: 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. (more…)
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.) (more…)
Author Interviews, Depression, Mental Health Research, Nature, PTSD / 21.10.2020

MedicalResearch.com Interview with: Amit Etkin, MD, PhD Department of Psychiatry and Behavioral Sciences Wu Tsai Neurosciences Institute, Stanford Universitu Stanford, CA    MedicalResearch.com: What is the mission of Cohen Veterans Bioscience - CVB?  Cohen Veterans Bioscience Response: Cohen Veterans Bioscience (CVB) is a non-profit 501(c)(3) research biotech dedicated to fast-tracking the development of diagnostic tests and personalized therapeutics for the millions of Veterans and civilians who suffer the devastating effects of trauma-related and other brain disorders. To learn about CVB’s research efforts visit www.cohenveteransbioscience.org.   MedicalResearch.com: How can patients with PTSD or MDD benefit from this information? Response: With the discovery of this new brain imaging biomarker, patients who suffer from PTSD or MDD may be guided towards the most effective treatment without waiting months and months to find a treatment that may work for them.   MedicalResearch.com: What is the background for this study? Response: This study, which was supported with a grant from Cohen Veterans Bioscience, grants from the National Institute of Mental Health (NIMH and other supporters, derives from our work over the past few years which has pointed to the critical importance of understanding how patients with a variety of psychiatric disorders differ biologically. The shortcomings of our current diagnostic system have become very clear over the past 1-2 decades, but the availability of tools for transcending these limitations on the back of objective biological tests has not kept pace with the need for those tools. In prior work, we have used a variety of methods, including different types of brain imaging, to identify brain signals that underpin key biological differences within and across traditional psychiatric diagnoses. We have also developed specialized AI tools for decoding complex patterns of brain activity in order to understand and quantify biological heterogeneity in individual patients. These developments have then, in turn, converged with the completion of a number of large brain imaging-coupled clinical trials, which have provided a scale of these types of data not previously available in the field. (more…)
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, 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 JangguWith 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, 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…)
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…)
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…)
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, 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…)
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.  (more…)
Author Interviews, NYU, PTSD, Technology / 22.04.2019

MedicalResearch.com Interview with: 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  (more…)
Author Interviews, Brigham & Women's - Harvard, Cancer Research, JAMA, Radiation Therapy, Technology / 19.04.2019

MedicalResearch.com Interview with: 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
(more…)
Author Interviews, JAMA, Pediatrics, Stanford, Technology / 26.03.2019

MedicalResearch.com Interview with: 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.   (more…)
Author Interviews, Lung Cancer, Nature, Technology / 05.03.2019

MedicalResearch.com Interview with: 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. (more…)
Author Interviews, MRI, Prostate Cancer, Technology / 12.02.2019

MedicalResearch.com Interview with: 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. (more…)
Author Interviews, FDA, JAMA, Medical Imaging / 04.12.2018

MedicalResearch.com Interview with: Aldo Badano, Ph.D. Deputy Director, Division of Imaging, Diagnostics, and Software Reliability Office of Science and Engineering Laboratories Center for Devices and Radiological Health Silver Spring, MD 20993 Aldo Badano, Ph.D. Deputy Director, Division of Imaging, Diagnostics, and Software Reliability Office of Science and Engineering Laboratories Center for Devices and Radiological Health Silver Spring, MD 20993  MedicalResearch.com: What is the background for this study? What are the main findings?  Response: Expensive and lengthy clinical trials can delay regulatory evaluation of innovative technologies, affecting patient access to high-quality medical products. Although computational modeling is increasingly being used in product development, it is rarely at the center of regulatory applications. Within this context, the VICTRE project attempted to replicate a previously conducted imaging clinical trial using only computational models. The VICTRE trial involved no human subjects and no clinicians. All trial steps were conducted in silico. The fundamental question the article addresses is whether in silico imaging trials are at a mature development stage to play a significant role in the regulatory evaluation of new medical imaging systems. The VICTRE trial consisted of in silico imaging of 2986 virtual patients comparing digital mammography (DM) and digital breast tomosynthesis (DBT) systems. The improved lesion detection performance favoring DBT for all breast sizes and lesion types was consistent with results from a comparative trial using human patients and radiologists.  (more…)
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 (more…)
Author Interviews, Brain Injury, CT Scanning, Emergency Care, Lancet, Technology / 16.10.2018

MedicalResearch.com Interview with: Qure-ai.jpgSasank Chilamkurthy AI Scientist, Qure.ai MedicalResearch.com: What is the background for this study? Response: Head CT scan is one of the most commonly used imaging protocols besides chest x-ray. They are used for patients with symptoms suggesting stroke, rise in intracranial pressure or head trauma. These manifest in findings like intracranial haemorrhage, midline shift or fracture. Scans with these critical findings need to be read immediately. But radiologists evaluate the scans on first-come-first-serve basis or based on stat/routine markers set by clinicians. If the scans with critical findings are somehow pushed to the top of radiologists’ work list, it could substantially decrease time to diagnosis and therefore decrease mortality and morbidity associated with stroke/head trauma. (more…)