Automatic prosthetic‐parameter estimation from anteroposterior pelvic radiographs after total hip arthroplasty using deep learning‐based keypoint detection

Abstract

BACKGROUND: X-ray is a necessary tool for post-total hip arthroplasty (THA) check-ups; however, parameter measurements are time-consuming. We proposed a deep learning tool, BKNet that automates localization of landmarks with parameter measurements. METHODS: About 3072 radiographs from 3021 patients who underwent THA at our institute between 2013 and 2017 were used. We employed BKNet to perform landmark localization with parameter measurements in these radiographs. The performance of BKNet was assessed and compared with that of human observers. RESULTS: The 75-percentile cut-off errors were <0.5 cm in all key points. The Bland-Altman methods show the agreement between the predicted and ground truth parameters. Human and BKNet comparison revealed the model could match the repeatability for 7/10 of the parameters. CONCLUSIONS: The accuracy of BKNet is equivalent to that of human observers, and BKNet was able to perform prosthetic-parameter estimation from keypoint detection with superior cost-effectiveness, repeatability, and timesaving compared to human observers.

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