Development of a Deep Learning Model for Hip Arthroplasty Templating Using Anteroposterior Hip Radiograph.

ABSTRACT: Background: Preoperative templating is an essential step in hip arthroplasty (HA), guiding implant selection and reducing surgical complications. It is typically performed using acetate templates or digital software. These methods, however, depend on the surgeon's experience and may be limited by cost and availability. This study aimed to develop and validate a deep learning (DL) model using plain radiographs to predict implant sizes in HA. Methods: This retrospective study included patients who underwent primary HA using a cementless CORAIL^® femoral stem and PINNACLE^® acetabular cup. The DL model was trained on 688 preoperative anteroposterior (AP) hip radiographs and validated temporally on 98 additional cases. Implant sizes predicted by the DL model w

SUBMITTER: Wongsak S 

PROVIDER: S-EPMC12734011 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature