Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis

Summary

Artificial intelligence is noninferior to clinicians in terms of diagnostic performance in fracture detection, showing promise as a useful diagnostic tool.

Key Results

  • ■ In a systematic review and meta-analysis of 42 studies (37 studies with radiography and five studies with CT), the pooled diagnostic performance from the use of artificial intelligence (AI) to detect fractures had a sensitivity of 92% and 91% and specificity of 91% and 91%, on internal and external validation, respectively.

  • ■ Clinician performance had comparable performance to AI in fracture detection (sensitivity 91%, 92%; specificity 94%, 94%).

  • ■ Only 13 studies externally validated results, and only one study evaluated AI performance in a prospective clinical trial.

Introduction

Fractures have an incidence of between 733 and 4017 per 100 000 patient-years (13). In the financial year April 2019 to April 2020, 1.2 million patients presented to an emergency department in the United Kingdom with an acute fracture or dislocation, an increase of 23% from the year before (4). Missed or delayed diagnosis of fractures on radiographs is a common diagnostic error, ranging from 3% to 10% (57). There is an inverse relationship between clinician experience and rate of fracture misdiagnosis, but timely access to expert opinion is not widely available (6). Growth in imaging volumes continues to outpace radiologist recruitment: A Canadian study (8) from 2019 found an increase in radiologist workloads of 26% over 12 years, whereas a study from the American College of Radiologists found a 30% increase in job openings from 2017 to 2018 (9). Strategies (6,10) to reduce rates of fracture misdiagnosis and to streamline patient pathways are crucial to maintain high standards of patient care.

Artificial intelligence (AI) is a

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