Accurate Determination of Hip Implant Wear, Cup Anteversion and Inclination Through AI Automated 2D-3D Registration.

The precise and accurate measurement of implant wear, acetabular cup anteversion and inclination from routine anterior-posterior radiographs still poses a challenge. Current approaches suffer from time-consuming procedures accompanied by low and observer-dependent accuracy and precision. We present and validate a novel, automated method for determining total hip arthroplasty parameters by comparing its accuracy and precision with methods in contemporary scientific literature. The algorithm uses CAD-model-based 2D-3D-registration supported by convolutional neural networks. Two in-vitro experimental setups were designed to validate the proposed 2D-3D-method. The setups provided 84 predefined wear values and 24 configurations of anteversion and inclination in 114 radiographs. Accuracy and precision were evaluated by systematically comparing the predefined ground truth and the automatically calculated values from in-vitro X-rays. In addition, an algorithm was developed and validated against physician's measurements on clinical X-rays to determine the inclination of the inter-teardrop (ITL) and biischial line (BL) to account for the individual patient's pelvic rotation in the frontal plane. Using X-rays from experimental setups, the determined mean error was 0.008 mm (standard deviation: 0.018 mm; root-mean-square-error: 0.020 mm) for wear, 0.01° (0.24°; 0.23°) for radiographic cup anteversion, and 0.11° (0.38°; 0.39°) for radiographic cup inclination. The inclination of ITL and BL was automatically determined in all clinical X-rays with excellent interclass correlation coefficients of 0.95 and 0.91, respectively. The presented algorithm allows the accurate and precise evaluation of total hip arthroplasty parameters without additional equipment. The method might help to investigate different implant designs, biomaterials, and surgical techniques with greater objectivity. This article is protected by copyright. All rights reserved.