Automated deep-neural-network surveillance of cranial images for acute neurologic events.
Nat Med. 2018 Aug 13. doi: 10.1038/s41591-018-0147-y. [Epub ahead of print]
Titano JJ1, Badgeley M2, Schefflein J1, Pain M2, Su A1, Cai M1, Swinburne N1, Zech J1, Kim J3, Bederson J2, Mocco J2, Drayer B1, Lehar J4, Cho S2,3, Costa A2, Oermann EK5.
Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function-'time is brain'1-5. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging6-10. Computer-aided surveillance of acute neurologic events in cranial imaging has the potential to triage radiology workflow, thus decreasing time to treatment and improving outcomes. Substantial clinical work has focused on computer-assisted diagnosis (CAD), whereas technical work in volumetric image analysis has focused primarily on segmentation. 3D convolutional neural networks (3D-CNNs) have primarily been used for supervised classification on 3D modeling and light detection and ranging (LiDAR) data11-15. Here, we demonstrate a 3D-CNN architecture that performs weakly supervised classification to screen head CT images for acute neurologic events. Features were automatically learned from a clinical radiology dataset comprising 37,236 head CTs and were annotated with a semisupervised natural-language processing (NLP) framework16. We demonstrate the effectiveness of our approach to triage radiology workflow and accelerate the time to diagnosis from minutes to seconds through a randomized, double-blinded, prospective trial in a simulated clinical environment.