Early diagnosis of Alzheimer’s disease using artificial intelligence

According to a study published in the journal of radiology, research shows that artificial intelligence (AI) technology predict the development of Alzheimer's disease early.Early diagnosis of Alzheimer's is important as¬†treatments and interventions are more effective early in the course of the disease. However, early diagnosis has proven to be challenging. Research has linked the disease … Continue reading Early diagnosis of Alzheimer’s disease using artificial intelligence

Collaboration between UCSF, Intel to develop deep learning analytics for healthcare

UC San Francisco's Center for Digital Health Innovation (CDHI) today announced a collaboration with Intel Corporation to deploy and validate a deep learning analytics platform designed to improve care by helping clinicians make better treatment decisions, predict patient outcomes, and respond more nimbly in acute situations.The collaboration brings together Intel's leading-edge computer science and deep … Continue reading Collaboration between UCSF, Intel to develop deep learning analytics for healthcare

New tool to discover bio-markers for aging using In-silico Pathway Activation Network Decomposition Analysis (iPANDA)

Today the Biogerontology Research Foundation announced the international collaboration on signaling pathway perturbation-based transcriptomic biomarkers of aging. On November 16th scientists at the Biogerontology Research Foundation alongside collaborators from Insilico Medicine, Inc, the Johns Hopkins University, Albert Einstein College of Medicine, Boston University, Novartis, Nestle and BioTime Inc. announced the publication of their proof of … Continue reading New tool to discover bio-markers for aging using In-silico Pathway Activation Network Decomposition Analysis (iPANDA)

Computer based model InFlo predicts cell signals and network activity

A multi-institution academic-industrial partnership of researchers led by Case Western Reserve University School of Medicine has developed a new method to broadly assess cell communication networks and identify disease-specific network anomalies. The computer-based method, called InFlo, was developed in collaboration with researchers at Philips and Princeton University and predicts how cells send signals across networks … Continue reading Computer based model InFlo predicts cell signals and network activity

Researchers use multi-task deep neural networks to automatically extract data from cancer pathology reports

Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs--a national network of organizations that systematically collect demographic and … Continue reading Researchers use multi-task deep neural networks to automatically extract data from cancer pathology reports

Novel diagnostic test for malaria using holographic imaging and artificial intelligence using deep learning

Duke researchers have devised a computerized method to autonomously and quickly diagnose malaria with clinically relevant accuracy -- a crucial step to successfully treating the disease and halting its spread.In 2015 alone, malaria infected 214 million people worldwide, killing an estimated 438,000. While Western medicine can spot malaria with near-perfect accuracy, it can be difficult … Continue reading Novel diagnostic test for malaria using holographic imaging and artificial intelligence using deep learning