Automated deep-neural-network surveillance of cranial images for acute neurologic events
| Autor: | Nathaniel C. Swinburne, Anthony Costa, Joshua B. Bederson, Joseph Lehar, Margaret Pain, Andres Su, Joseph J. Titano, J Mocco, Michael Cai, Javin Schefflein, Samuel K. Cho, Burton P. Drayer, Eric K. Oermann, Jun S. Kim, John R. Zech, Marcus A. Badgeley |
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| Rok vydání: | 2018 |
| Předmět: |
medicine.medical_specialty
Time to treatment Convolutional neural network General Biochemistry Genetics and Molecular Biology 030218 nuclear medicine & medical imaging Automation 03 medical and health sciences Imaging Three-Dimensional 0302 clinical medicine Clinical work medicine Humans Segmentation Stroke Randomized Controlled Trials as Topic Artificial neural network business.industry Skull General Medicine medicine.disease Triage Hydrocephalus ROC Curve Neural Networks Computer Radiology Tomography X-Ray Computed business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Nature Medicine. 24:1337-1341 |
| ISSN: | 1546-170X 1078-8956 |
| DOI: | 10.1038/s41591-018-0147-y |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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