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Automated deep-neural-network surveillance of cranial images for acute neurologic events


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’1,2,3,4,5. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging6,7,8,9,10. 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) data11,12,13,14,15. 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.

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Fig. 1: Data pipeline, CNN triage concept, and analysis of current CT head workflow.
Fig. 2: Systems for encoding images and mapping diseases to urgency.
Fig. 3: Human and algorithm classifier performance and diagnosis heterogeneity.
Fig. 4: Interpretation speed and CNN queue triage.

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We thank E. Gordon for assistance with data collection for the purposes of building the NLP pipeline. We also thank the National Library of Medicine for making the UMLS Metathesaurus available.

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Authors and Affiliations



A.C. and E.K.O. designed and built the computing environment for this project. J.J.T., J.M., and E.K.O. conceived the study. A.C., J.L., and E.K.O. developed the computer-vision algorithms. J.Z., E.K.O., J.S., and M.P. developed the NLP algorithms. J.J.T., M.P., A.S., J.K., and S.C. built the crowdsourcing platform for obtaining ground-truth labels. J.J.T., J.S., M.C., N.S., and J.Z. generated gold-standard labels and assembled the datasets. M.B. and E.K.O. designed and oversaw the clinical simulation. M.B. performed the statistical analysis and designed the figures. J.J.T., J.B., B.D., and E.K.O. supervised the project. All authors contributed to writing and editing the manuscript.

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Correspondence to Eric K. Oermann.

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Competing interests

J.L. currently works for Merck in addition to his role as an adjunct professor at Boston University. M.B. currently works for Verily Life Sciences in addition to his role as a medical student at Mount Sinai. All of the present work was performed within the Mount Sinai Health System, and Merck and Verily played no role in the research and have no commercial interest in it.

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Supplementary Information

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Supplementary Figures 1–4 and Supplementary Tables 2–5

Reporting Summary

Supplementary Table 1

UMLS diagnosis - criticality abstraction

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Titano, J.J., Badgeley, M., Schefflein, J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med 24, 1337–1341 (2018).

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