Deep Learning Method for Hip Knee Ankle Angle Prediction on Postoperative Full-Limb Radiographs of Total Knee Arthroplasty Patients.

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Abstract

This study developed and evaluated deep learning models for prediction of hip knee ankle angle (HKAA) measurements on postoperative full-limb radiographs of total knee arthroplasty (TKA) patients. The process involved extracting regions of interest (RoI) on 1899 radiographs, applying landmark detection by regressing heatmaps based on the extracted RoI, and finally calculating the HKAA. We used mean and standard deviation of the differences between HKAA angle predictions and annotations as the evaluation metric. Postoperative HKAA difference between model predictions and annotations was 0.65° ± 0.82° and the percentage of difference smaller than 1.5° was 95.0%. In conclusion we developed a fully automated tool to measure HKAA accurately and precisely on postoperative full-limb radiographs of TKA patients.