Find out what’s new

Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST)

In the study population – NanoxAI Bone tool helped detect more patients at-risk of osteoporosis when comparing to standard of care (FRAX)

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)3-6, are underutilized.

Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)3,4,5,6, are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans.

PHT-BOT: Deep-Learning Based System For Automatic Risk Stratification Of COPD Patients Based Upon Signs Of Pulmonary Hypertension

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade

Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized.

Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT

The amount of calcium deposits in the coronary arteries is a powerful predictor of cardiovascular events and mortality.

TextRay: Mining Clinical Reports To Gain A Broad Understanding Of Chest X-Rays

The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases.

Improved Intracranial Hemorrhage Classification using Deep Multi-task Learning

Head CT is one of the most commonly performed imaging studied in the Emergency Department setting and Intracranial hemorrhage (ICH) is among the most critical and timesensitive findings to be detected on Head CT.

Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement

Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide.

RadBot-CXR: Classification of Four Clinical Finding Categories in Chest X-Ray Using Deep Learning

The well-documented global shortage of radiologists is most acutely manifested in countries where the rapid rise of a middle class has created a new capacity to produce imaging studies at a rate which far exceeds the time required to train experts capable of interpreting such studies

Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT

The amount of calcium deposits in the coronary arteries is a powerful predictor of cardiovascular events and mortality. Agatson score measured on cardiac CT is the routine method to identify subjects at high risk who might benefit from proactive treatment.

Compression Fractures Detection on CT

The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere.