Comprehensive Summary
Time-honored methods to determine appropriate implant dimensions in total knee arthroplasty commonly depend on templating techniques that can be time-consuming, imprecise, or contingent on advanced imaging modalities. The following research study evaluates the utilization of AI algorithms to effectively predict implant sizes in TKA based solely on standard knee X-ray images. In accordance with the study, a deep learning model, titled “ResNet-101,” was trained on 1,412 anteroposterior and lateral knee radiographs from 714 patients, derived from extensive data augmentation while omitting demographic and clinical factors. The model posted impressive accuracy, with micro F1-scores of 0.91 for the femur and 0.87 for the tibia in exact sizing, rising to 0.99 and 0.98, respectively, within a +/- 1 size tolerance. Lateral X-rays provided the most accurate input among the plurality of modalities listed. The findings outstripped those of traditional strategies and prior AI systems sustained by demographic data, even though the model was coached on a modest, homogeneous sample.
Outcomes and Implications
Accurate implant sizing in advance of total knee replacement is substantial to boost surgical efficacy and alleviate surgical risks like implant mismatch or implant overhang. Be that as it may, however, contemporary templating practices are often laborious and rest primarily on the clinician’s expertise. Harnessing only routine X-rays, such a model delivers an effective and efficient tool for predicting implant size with fine precision, which could potentially streamline preoperative workflows and reduce the burden on surgeons. Though promising, the model certainly requires off-hand justification across diverse populations and healthcare settings before pervasive use, which may call for additional multicenter studies and algorithm refinement.