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Deep learning model improves radiologist diagnostic performance in colon cancer screening
A study by the Technical University of Munich researchers evaluated the use of a deep learning algorithm to differentiate between colon cancer and acute diverticulitis on CT images and its impact on radiologists’ performance. The 3-D convolutional neural network reached a sensitivity of 83.3% and specificity of 86.6% compared to the average reader sensitivity of…
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Sybil, a machine-learning model for lung cancer risk assessment
Researchers at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, Mass General Cancer Center, and Chang Gung Memorial Hospital have developed an artificial intelligence tool named Sybil for lung cancer risk assessment. Sybil analyzes low-dose computed tomography (LDCT) image data without using clinical or demographic data to predict a patient’s risk of developing…
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A new computational model to predict patient-specific growth of glioblastoma multiforme
Researchers at the University of Waterloo and the University of Toronto have developed a new computational model to predict the growth of glioblastoma multiforme (GBM) more accurately. The proliferation-invasion (PI) model is a mathematical model commonly used to describe the growth of glioblastoma multiforme (GBM). It relies on known values of two key parameters, the…
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New Computational Equation To Better Predict Drug-Drug Interactions
A joint research team of mathematicians and pharmacological scientists has identified the major causes of inaccuracies in the Food and Drug Administration’s (FDA) equation for predicting drug-drug interactions and presented solutions. They found that the FDA’s equation, based on the 110-year-old Michaelis-Menten (MM) model, was only accurate 38% of the time. The MM model is…
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Wearable Electronics Created Using Screen Printing
Researchers from Washington State University have developed a way to create the serpentine structures that power wearable electronics using screen printing, the same technology used to print rock concert t-shirts. The method creates a stretchable, durable circuit pattern that can be transferred to fabric and worn directly on human skin. Current commercial manufacturing of wearable…
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Research Suggests COVID-19 May Trigger Multiple Sclerosis
A recent study published in Scientific Reports suggests that COVID-19 may trigger Multiple Sclerosis (MS) in susceptible individuals through a process known as “molecular mimicry.” The study conducted by scientists at the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, analyzed the structure of SARS-CoV-2 proteins and more than…
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Machine learning model to predict drug side effects
Researchers developed a machine learning model to predict drug side effects that were discovered in post-marketing surveillance after clinical trials. Diego Galeano and Alberto Paccanaro utilized a geometric self-expressive model(GSEM) machine learning framework and post-marketing drug side effect data, drug chemical structure and protein targets data, and drug indications data. GSEM algorithm to predict drug…
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Visible app – Activity monitoring for Illness
The Visible platform helps monitor health using manual data inputs about symptoms. It also utilizes smartphone cameras to analyze heart rate variation using a technique called photoplethysmography. In the future Visible hope to provide a monthly subscription service that includes a Polar Verity Sense heart-rate monitor (HRM), which is worn on the arm to collect…
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A new computational approach to autism screening utilizing digital biomarkers shows promise
Researchers at the University of Chicago have developed a novel computational approach that can reliably predict an eventual diagnosis of autism spectrum disorder (ASD) in young children, without the need for additional blood work or procedures, using only diagnostic codes from past doctor’s visits. The new approach reportedly reduces the number of false-positive ASD diagnoses…
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VarSAn – Computational tool to identify disease pathways
Researchers at the University of Illinois Urbana-Champaign have developed a new computational tool to identify pathways related to diseases, including breast and prostate cancer, using single-nucleotide polymorphisms (SNPs). The tool, called VarSAn (Variant Set Annotator, pronounced ‘version’), uses SNPs that have been identified by sequencing studies as being disease-related, to predict which pathways may be…

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