Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial

Abstract

T he accurate determination of a child's developmental status is required for proper treatment of various growth disorders (1) and scoliosis (2). Other parameters, such as height, weight, secondary sexual characteristics, chronologic age, and dental age, correlate with developmental status, but skeletal age has been considered the most reliable method (3-5). The standard of care for this assessment calls for radiologists to identify the reference standard in an atlas of hand radiographs that most closely resembles an anteroposterior or posteroanterior radiograph of the participant's left hand. The most common atlas used as a reference standard is the Radiographic Atlas of Skeletal Development of the Hand and Wrist, published in 1959 (6).As part of the process of implementing an artificial intelligence (AI) algorithm in clinical practice, it is critical to properly determine its effects. However, different study designs may yield different findings about the same assistive technologies. For example, the same commercially available computer-aided detection system for detecting pulmonary nodules on chest CT scans produced different findings in studies completed within a year of each other (7-9). Findings on potential computer-aided diagnosis Background: Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice.Purpose: To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods:In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists' signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center-and radiologist-level effects was used to compare the two experimental groups.Results: Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001). Conclusion:Use of an artificial intelligence algorithm ...

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