Deep Learning Analysis of Routine Preoperative Knee Radiographs for Five-Year Prediction of Contralateral Arthroplasty

Abstract

Background: Before unilateral total knee arthroplasty (TKA), the contralateral radiograph is reduced to a Kellgren–Lawrence grade. We asked what the image adds, what the model reads, and how many views are needed. Methods: A multi-view DenseNet121 survival network predicted 5-year contralateral arthroplasty from routine radiographs, against Cox models over 11 clinical variables with and without the dataset-inferred grade, on a once-read test split. Results: In 741 test patients (106 events), the primary contrast put the combined model’s 5-year censoring-weighted AUROC at 0.844 against the clinical model’s 0.680 among the 740 patients that received both scores (difference 0.164, 0.087 to 0.240). Post hoc, a frontal radiograph alone read 0.837, matching the multi-view arms because 56.6% had only a frontal film; withholding lateral and sunrise views cost 0.078 (0.012 to 0.142) in the 315 patients with both. Grad-CAM put 37.8% of attribution in a joint region covering 25.0%; occluding it left 0.800. Conclusions: The preoperative radiograph adds prognostic information beyond the clinical record. Exploratory analyses place almost all of it in one frontal film and beyond an automated grade, not a radiologist’s read. These internal analyses require external validation.