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Alzheimer's - Dementia, Author Interviews, Cognitive Issues, McGill, Neurology, Technology / 28.01.2020

MedicalResearch.com Interview with: Yasser Iturria-Medina PhD Assistant Professor, Department of Neurology and Neurosurgery Associate member of the Ludmer Centre for Neuroinformatics and Mental Health McConnell Brain Imaging Centre McGill University MedicalResearch.com: What is the background for this study? Response: As background, two main points:
  • Almost all molecular (gene expression) analyses performed in neurodegeneration are based on snapshots data, taking at one or a few time points covering the disease's large evolution. Because neurodegenerative diseases take decades to develop, until now we didn't have a dynamical characterization of these diseases. Our study tries to overcome such limitation, proposing a data-driven methodology to study long term dynamical changes associated to disease.
Also, we still lacked robust minimally invasive and low-cost biomarkers of individual neuropathological progression. Our method is able to offer both in-vivo and post-mortem disease staging highly predictive of neuropathological and clinical alterations. (more…)
Author Interviews, Dermatology, Melanoma, Technology / 19.09.2019

SkinVision   MedicalResearch.com Interview with:  Andreea Udrea, PhD Associate Professor University Politehnica of Bucharest   MedicalResearch.com: What is the background for this study? The skin cancer incidence rate is increasing worldwide. Early diagnosis and prevention can reduce morbidity and are also linked to decreased healthcare costs. During the last years, efforts have been made in developing smartphone applications for skin lesion risk assessment to be used by laypersons. In parallel, as machine learning (ML) is on the rise, and medical image databases are increasing in size, a series of algorithms have been developed and compared in clinical studies to dermatologists for skin cancer diagnosis. The accuracy of the algorithms and experts were comparable. One drawback of these clinical studies is that they use images acquired by professionals in standardized conditions. So, there is little knowledge of what the accuracy will be when including an ML algorithm in an app and testing it in a non-clinical setup where the image quality may be lower, and the variability in image taking scenarios is higher as images are acquired by non-professionals using the smartphone camera. This study is one of the first that evaluates the accuracy of an app (SkinVision) when being used for risk assessment of skin lesions in the general population. (more…)
Author Interviews, Cancer Research, Genetic Research, JAMA, Technology / 30.04.2019

MedicalResearch.com Interview with: 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.  (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, Schizophrenia, Technology / 13.07.2018

MedicalResearch.com Interview with: Bo Cao, Ph.D. Assistant Professor Department of Psychiatry Faculty of Medicine & Dentistry University of Alberta Edmonton MedicalResearch.com: What is the background for this study? What are the main findings? Response: Schizophrenia is a severe psychiatric disorder that comes with delusions, hallucinations, poor motivation, cognitive impairments. The economic burden of schizophrenia was estimated at $155.7 billion in 2013 alone in the United States. Schizophrenia usually emerges early in life and can potentially become a lifetime burden for some patients. Repeated untreated psychotic episodes may be associated with irreversible alterations of the brain. Thus, it is crucial to identify schizophrenia early and provide effective treatment. However, identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Limited progress has been made in leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics. In a recent study by Dr. Cao and his colleagues, they successfully identified the first-episode drug-naïve schizophrenia patients (accuracy 78.6%) and predict their responses to antipsychotic treatment (accuracy 82.5%) at an individual level by using a machine learning algorithm and the functional connections of a brain region called the superior temporal cortex.  (more…)
Author Interviews, Brain Cancer - Brain Tumors, Emory, PNAS, Technology / 16.03.2018

MedicalResearch.com Interview with: Lee Cooper, Ph.D. Assistant Professor of Biomedical Informatics Assistant Professor of Biomedical Engineering Emory University School of Medicine - Georgia Institute of Technology MedicalResearch.com: What is the background for this study? What are the main findings?  Response: Gliomas are a form of brain tumor that are often ultimately fatal, but patients diagnosed with glioma may survive as few as 6 months to 10 or more years. Prognosis is an important determinant in selecting treatment, that can range from simply monitoring the disease to surgical removal followed by radiation treatment and chemotherapy. Recent genomic studies have significantly improved our ability to predict how rapidly a patient's disease will progress, however a significant part of this determination still relies on the visual microscopic evaluation of the tissues by a neuropathologist. The neuropathologist assigns a grade that is used to further refine the prognosis determined by genomic testing. We developed a predictive algorithm to perform accurate and repeatable microscopic evaluation of glioma brain tumors. This algorithm learns the relationships between visual patterns presented in the brain tumor tissue removed from a patient brain and the duration of that patient's survival beyond diagnosis. The algorithm was demonstrated to accurately predict survival, and when combining images of histology with genomics into a single predictive framework, the algorithm was slightly more accurate than models based on the predictions of human pathologists. We were also able to identify that the algorithm learns to recognize some of the same tissue features used by pathologists in evaluating brain tumors, and to appreciate their prognostic relevance. (more…)
Author Interviews, Radiology, Technology / 17.02.2018

MedicalResearch.com Interview with: Eric Karl Oermann, MD Instructor Department of Neurosurgery Mount Sinai Health System New York, New York 10029  MedicalResearch.com: What is the background for this study? What are the main findings?  Response: Supervised machine learning requires data consisting of features and labels. In order to do machine learning with medical imaging, we need ways of obtaining labels, and one promising means of doing so is by utilizing natural language processing (NLP) to extract labels from physician's descriptions of the images (typically contained in reports). Our main finding was that (1) the language employed in Radiology reports is simpler than normal day-to-day language, and (2) that we can build NLP models that obtain excellent results at extracting labels when compared to manually extracted labels from physicians.  (more…)
Author Interviews, Diabetes, JAMA, Ophthalmology, Technology / 13.12.2017

MedicalResearch.com Interview with: Dr. Tien Yin Wong MD PhD Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore Singapore MedicalResearch.com: What is the background for this study? What are the main findings? Response: Currently, annual screening for diabetic retinopathy (DR) is a universally accepted practice and recommended by American Diabetes Association and the International Council of Ophthalmology (ICO) to prevent vision loss. However, implementation of diabetic retinopathy screening programs across the world require human assessors (ophthalmologists, optometrists or professional technicians trained to read retinal photographs). Such screening programs are thus challenged by issues related to a need for significant human resources and long-term financial sustainability. To address these challenges, we developed an AI-based software using a deep learning, a new machine learning technology. This deep learning system (DLS) utilizes representation-learning methods to process large data and extract meaningful patterns. In our study, we developed and validated this using about 500,000 retinal images in a “real world screening program” and 10 external datasets from global populations. The results suggest excellent accuracy of the deep learning system with sensitivity of 90.5% and specificity of 91.6%, for detecting referable levels of DR and 100% sensitivity and 91.1% specificity for vision-threatening levels of DR (which require urgent referral and should not be missed). In addition, the performance of the deep learning system was also high for detecting referable glaucoma suspects and referable age-related macular degeneration (which also require referral if detected). The deep learning system was tested in 10 external datasets comprising different ethnic groups: Caucasian whites, African-Americans, Hispanics, Chinese, Indians and Malaysians (more…)
Author Interviews, Breast Cancer, Brigham & Women's - Harvard, Cancer Research, Mammograms, Technology / 20.10.2017

MedicalResearch.com Interview with: Manisha Bahl, MD, MPH Director, Breast Imaging Fellowship Program, Massachusetts General Hospital Assistant Professor of Radiology, Harvard Medical School MedicalResearch.com: What is the background for this study? What are the main findings? Response: Image-guided biopsies that we perform based on suspicious findings on mammography can yield one of three pathology results: cancer, high-risk, or benign. Most high-risk breast lesions are noncancerous, but surgical excision is typically recommended because some high-risk lesions can be upgraded to cancer at surgery. Currently, there are no imaging or other features that reliably allow us to distinguish between high-risk lesions that warrant surgery from those that can be safely followed, which has led to unnecessary surgery of high-risk lesions that are not associated with cancer. We decided to apply machine learning algorithms to help us with this challenging clinical scenario: to distinguish between high-risk lesions that warrant surgery from those that can be safely followed. Machine learning allows us to incorporate the full spectrum of diverse and complex data that we have available, such as patient risk factors and imaging features, in order to predict which high-risk lesions are likely to be upgraded to cancer and, ultimately, to help our patients make more informed decisions about surgery versus surveillance. We developed the machine learning model with almost 700 high-risk lesions, then tested it with more than 300 high-risk lesions. Instead of surgical excision of all high-risk lesions, if those categorized with the model to be at low risk for upgrade were surveilled and the remainder were excised, then 97.4% malignancies would have been diagnosed at surgery, and 30.6% of surgeries of benign lesions could have been avoided. (more…)