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10th March 2022
Inclusion of artificial intelligence (AI) assistance improves the detection of fractures for both radiologists and non-radiologists without increasing the reading time. This was the finding from a retrospective analysis by a team from the Departments of Radiology, Orthopaedic Surgery and Family Medicine (D.C.), Boston University School of Medicine, Boston, US.
Diagnostic errors, especially within a busy emergency department can include missed fractures. Indeed, one study of 953 diagnostic errors revealed how 79.7% of these errors were because of missed fractures with the most common reason (77.8%) for the error being misreading radiographs. Furthermore, although the aforementioned study was from 2001, a 2018 Dutch study found that from a total of 25,957 fractures, 289 (1.1%) fractures were missed by emergency care physicians. The authors concluded that adequate training of physicians in radiographic interpretation was essential in order to increase diagnostic accuracy. The use of AI assistance for the detection of fractures has been examined in a number of studies, evaluating fractures in different parts of the body. One study evaluated fractures in 11 body areas, with the authors concluding that there were significant improvements in diagnostic accuracy with deep learning methods, however, the study did not include radiologists to interpret the results.
For the present study, the US team decided to expand upon previous analyses, including not just radiologists but a wide range of clinicians from different specialities such orthopaedic, emergency care, rheumatology and family physicians and fractures from different areas of the body. The AI algorithm was developed using data from 60,170 radiographs with trauma from 22 different institutions and split into a training, validation and internal test set.
The team used a retrospective design and the ground truth was established by two experienced musculoskeletal radiologists with 12 and 8 years of experience, who independently interpreted all of the study scans but without clinical information. For the study, the team included only acute fractures as a positive finding for the study. AI performance was assessed using receiver operating characteristic (ROC) curves, from which sensitivity and specificity values were determined using the area under the curve (AUC) values.
AI assistance and interpretation of fractures
A total of 480 patients with a mean age of 59 years (61.8% female) were included with 350 fractures. Included fractures were present on: feet and ankles, knee and leg, hip and pelvis, hand and wrist, elbow and arm, shoulder and clavicle, rib cage and thoracolumbar spine.
The sensitivity per patient was estimated at 64.8% without AI assistance and 75.2% with assistance, a 10.4% estimated AI effect (p < 0.001 for superiority). The associated specificity was 90.6% without AI and 95.6% with AI, a +5% estimated effect of AI (p = 0.001 for non-inferiority).
In addition, the use of AI assistance, shortened the average reading time by 6.3 seconds per examination. Furthermore, the sensitivity by patient gain was significant in all of the fracture regions examined ranging from +8% to +16.2% (p < 0.05), apart from the shoulder, clavicle and spine, where although there was an increase, this was non-significant.
Based on their findings, the authors concluded that AI assistance improves the sensitivity of fracture detection for both radiologists and other non-radiology clinicians as well as slightly reducing the time required to interpret the radiographs.
Guermazi A et al. Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence Radiology 2022