ABSTRACT
Methods:Two thousand standard sections images werre collected from 2 000 clinical retrospective pediatric hip ultrasound videos from January 2019 to January 2021. All standard sections were annotated by the annotation team through the self-designed software based on Python 3.6 environment for image cross-media data annotation and manual review standardization process with unified standards. Among them, 1 753 were randomly selected for training the deep learning system, and the remaining 247 were used for testing the system. Further, 200 standard sections were randomly selected from the test set, and 8 clinicians independently completed the film reading annotation. The 8 independent results were then compared with the AI results.Results:The testing set consists of 247 patients. Compared with the clinician's measurements, the area under the receiver operating characteristic curve (AUC) of diagnosing hip joint maturity was 0.865, the sensitivity was 76.19%, and the specificity was 96.9%. The AUC of AI system interpretation under Graf detailed typing was 0.575, the sensitivity was 25.90%, the specificity was 89.10%. The 95% LoA of α-angle determined by Bland-Altman method, of -4.7051° to 6.5948° ( Bias -0.94, P<0.001), compared with clinicians' measurements. The 95% LoA of β-angle, of -7.7191 to 6.8777 ( Bias -0.42, P=0.077). Compared with those from 8 clinicians, the results of AI system interpretation were more stable, and the β-angle effect was more prominent. Conclusion:The AI system can quickly and accurately measure the Graf correlation index of standard DDH ultrasonic standard diagnosis plane.