Deep Learning for Radiographic Measurement of Femoral Component Subsidence Following Total Hip Arthroplasty.

ABSTRACT

Femoral component subsidence following total hip arthroplasty (THA) is a worrisome radiographic finding. This study developed and evaluated a deep learning tool to automatically quantify femoral component subsidence between two serial anteroposterior (AP) hip radiographs. The authors' institutional arthroplasty registry was used to retrospectively identify patients who underwent primary THA from 2000 to 2020. A deep learning dynamic U-Net model was trained to automatically segment femur, implant, and magnification markers on a dataset of 500 randomly selected AP hip radiographs from 386 patients with polished tapered cemented femoral stems. An image processing algorithm was then developed to measure subsidence by automatically annotating reference points on the femur and implant, calibrating that with respect to magnification markers. Algorithm and manual subsidence measurements by two independent orthopedic surgeon reviewers in 135 randomly selected patients were compared. The mean, median, and SD of measurement discrepancy between the automatic and manual measurements were 0.6, 0.3, and 0.7 mm, respectively, and did not demonstrate a systematic tendency between human and machine. Automatic and manual measurements were strongly correlated and showed no evidence of significant differences. In contrast to the manual approach, the deep learning tool needs no user input to perform subsidence measurements. Keywords Total Hip Arthroplasty, Femoral Component Subsidence, Artificial Intelligence, Deep Learning, Semantic Segmentation, Hip, Joints Supplemental material is available for this article. © RSNA, 2022.