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March 1, 2020Radiology Artificial IntelligenceOpen Access

Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning

Justin D. Krogue Kaiyang Cheng Kevin Hwang Paul Toogood Eric Meinberg Erik J. Geiger Musa B. Zaid Kevin C. McGill Rina Patel Jae Ho Sohn Alexandra Wright Bryan Darger Kevin Padrez Eugene Ozhinsky Sharmila Majumdar Valentina Pedoia

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Abstract

A deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians.Supplemental material is available for this article.© RSNA, 2020.

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Krogue et al. (2020) studied this question.

synapsesocial.com/papers/69cc2816f171a3f5a8caedd5 https://doi.org/10.1148/ryai.2020190023

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Effect of comorbidities and postoperative complications on mortality after hip fracture in elderly people: prospective observational cohort studycohort · 2005 · 1,431 citations
2Incidence and Mortality of Hip Fractures in the United StatesObservational Study · 2009 · 1,655 citations
3Early Surgery Confers 1-Year Mortality Benefit in Hip-Fracture Patientscohort · 2017 · 84 citations
4Surgery for a fracture of the hip within 24 hours of admission is independently associated with reduced short-term post-operative complicationscohort · 2017 · 113 citations
5Surgical Management of Hip Fractures: An Evidence-based Review of the Literature. I: Femoral Neck FracturesPrognostic Study · 2008 · 280 citations

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