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

被引：90

作者：

Jones, Rebecca M. ^{[1
]}

Sharma, Anuj ^{[1
]}

Hotchkiss, Robert ^{[2
]}

Sperling, John W. ^{[3
]}

Hamburger, Jackson ^{[1
]}

Ledig, Christian ^{[1
]}

O'Toole, Robert ^{[4
]}

Gardner, Michael ^{[5
]}

Venkatesh, Srivas ^{[1
]}

Roberts, Matthew M. ^{[2
]}

Sauvestre, Romain ^{[1
]}

Shatkhin, Max ^{[1
]}

Gupta, Anant ^{[1
]}

Chopra, Sumit ^{[1
]}

Kumaravel, Manickam ^{[6
]}

Daluiski, Aaron ^{[2
]}

Plogger, Will ^{[1
]}

Nascone, Jason ^{[7
]}

Potter, Hollis G. ^{[2
]}

Lindsey, Robert, V ^{[1
]}

机构：

[1] Imagen Technol Inc, 151 West 26th St,Suite 1001, New York, NY 10001 USA

[2] Hosp Special Surg, 523 East 72nd St, New York, NY 10021 USA

[3] Mayo Clin, 200 1st St SW, Rochester, MN 55905 USA

[4] Univ Maryland Med Syst, R Adams Cowley Shock Trauma Ctr, 22 South Greene St, Baltimore, MD 21201 USA

[5] Stanford Univ, 450 Broadway St, Redwood City, CA 94063 USA

[6] Univ Texas Houston, Med Sch Houston, 6431 Fannin St, Houston, TX 77030 USA

[7] Univ Maryland Med Syst, 22 South Greene St, Baltimore, MD 21201 USA

来源：

NPJ DIGITAL MEDICINE | 2020年 / 3卷 / 01期

关键词：

RADIOLOGY;

D O I：

10.1038/s41746-020-00352-w

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This approach may have the potential to reduce future diagnostic errors in radiograph interpretation.

引用

页数：6

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