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Deep neural network improves fracture detection by clinicians

Imagen developed a deep neural network to detect and localize fractures in wrist radiographs. A controlled experiment showed that the average clinician had a 47% relative reduction in their misinterpretation rate using the deep-learning model. This significant improvement in diagnostic accuracy shows that deep-learning methods can substantially enhance patient care.

Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs

Imagen developed a deep-learning system for detecting fractures across the musculoskeletal system. In a multi-site study, the deep-learning system accurately identified fractures throughout the adult musculoskeletal system. The results were used, in part, to support FDA clearance of Imagen’s software (FractureDetect) to assist clinicians in detecting fractures for a selected subset of the anatomic regions reported in this paper.

Reevaluation of missed lung cancer with artificial intelligence

In this work, Imagen shows the effectiveness of Chest-CAD, our FDA-cleared software that analyzes chest radiograph studies using artificial intelligence. Our software highlighted a suspicious part of an X-ray missed through unaided interpretation, thus showing the promise of the additional information provided by our artificial intelligence software.