Deep neural network improves fracture detection by clinicians

By Rob Lindsey, A. Daluiski, Sumit Chopra, Alexander Lachapelle, M. Mozer, Serge Sicular, D. Hanel, Michael Gardner, Anurag Gupta, R. Hotchkiss and 1 more. Proceedings of the National Academy of Sciences of the United States of America, 2018

566 citations recorded by the source corpus.

Significance Historically, computer-assisted detection (CAD) in radiology has failed to achieve improvements in diagnostic accuracy, decreasing clinician sensitivity and leading to unnecessary further diagnostic tests. With the advent of deep learning approaches to CAD, there is great excitement about its application to medicine, yet there is little evidence demonstrating improved diagnostic accuracy in clinically-relevant applications. We trained a deep learning model to detect fractures on radiographs with a diagnostic accuracy similar to that of senior subspecialized orthopedic surgeons. We demonstrate that when emergency medicine clinicians are provided with the assistance of the trained model, their ability to accurately detect fractures significantly improves. Suspected fractures are among the most common reasons for patients to visit emergency departments (EDs), and X-ray imaging is the primary diagnostic tool used by clinicians to assess patients for fractures. Missing a fracture in a radiograph often has severe consequences for patients, resulting in delayed treatment and poor recovery of function. Nevertheless, radiographs in emergency settings are often read out of necessity by emergency medicine clinicians who lack subspecialized expertise in orthopedics, and misdiagnosed fractures account for upward of four of every five reported diagnostic errors in certain EDs. In this work, we developed a deep neural network to detect and localize fractures in radiographs. We trained it to accurately emulate the expertise of 18 senior subspecialized orthopedic surgeons by having them annotate 135,409 radiographs. We then ran a controlled experiment with emergency medicine clinicians to evaluate their ability to detect fractures in wrist radiographs with and without the assistance of the deep learning model. The average clinician’s sensitivity was 80.8% (95% CI, 76.7–84.1%) unaided and 91.5% (95% CI, 89.3–92.9%) aided, and specificity was 87.5% (95 CI, 85.3–89.5%) unaided and 93.9% (95% CI, 92.9–94.9%) aided. The average clinician experienced a relative reduction in misinterpretation rate of 47.0% (95% CI, 37.4–53.9%). The significant improvements in diagnostic accuracy that we observed in this study show that deep learning methods are a mechanism by which senior medical specialists can deliver their expertise to generalists on the front lines of medicine, thereby providing substantial improvements to patient care.

Subfields: Family Practice, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3dbe2b18393c34dc30f057949e6d31e3ef6efd90

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Citations in the corpus

Key challenges for delivering clinical impact with artificial intelligence (2019, background): “The limited number of prospective studies to date include diabetic retinopathy grading [48 – 50], detection of breast cancer metastases in sentinel lymph node biopsies [51, 52], wrist fracture detection [53], colonic polyp detection [28, 54], and detection of congenital cataracts [55].”
Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review (2020, background): “From our review, 16 studies demonstrated that AI applications could enhance clinical decision-making capacity [31-33,45-47,50,55,58,59,63,64,67,69,71,74,75].”
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis (2020, background): “AI systems cannot only augment the information provided by such images with useful annotations (1, 2), but they are also starting to take autonomous decisions by performing computer-aided diagnosis (CAD) (3, 4).”
From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People (2020, background): “Advancing AI technologies [4] have powered many systems.”