A Novel Approach for Screening Standard Anteroposterior Pelvic Radiographs in Children

Abstract

Abstract Anteroposterior pelvic radiography is the first-line imaging modality for diagnosing developmental dysplasia of the hip (DDH). Nonstandard radiographs with pelvic malposition make the correct diagnosis of DDH challenging. However, as the only method available for screening standard pelvic radiographs, traditional manual assessment is relatively laborious and potentially erroneous. We retrospectively collected 3,247 pelvic radiographs. There were 2,887 radiographs randomly selected to train and optimize the AI model. Then 362 radiographs were used to test the model’s diagnostic performance. Its diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves and measurement consistency using Bland-Altman plots. In 362 radiographs, the AI model’s area under ROC curves, accuracy, sensitivity, and specificity for quality assessment was 0.993, 99.4% (360/362), 98.6% (138/140), and 100.0% (220/220), respectively. Compared with clinicians, the 95% limits of agreement (Bland-Altman analysis) for pelvic tilt index (PTI) and pelvic rotation index (PRI), as determined by the model, were − 0.052–0.072 and − 0.088 − 0.055, respectively. Conclusions: The artificial intelligence-assisted method was more efficient and highly consistent with clinical experts. This method can be used for real-time validation of the quality of pelvic radiographs in current picture archiving and communications systems (PACS).