{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T05:39:38Z","timestamp":1788586778374,"version":"build-2803163510"},"reference-count":27,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"16","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,20]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Accurate and timely differential diagnosis of hip prosthesis failures remains a major clinical challenge. Radiographic examination remains the most cost-effective and common first-line imaging modality for hip prostheses, and integrating deep learning has the potential to improve its diagnostic accuracy and efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>A deep learning-based clinical classification system (Hip-Net) was developed to classify multiple causes of total hip arthroplasty failure, including periprosthetic joint infection (PJI), aseptic loosening, dislocation, periprosthetic fracture, and polyethylene wear. Hip-Net employed a dual-channel ensemble of 4 deep learning models trained on 2,908 routine dual-view (anteroposterior and lateral) radiographs for 1,454 patients (Asian) across 3 medical centers. An interpretive subnetwork generated spatially resolved disease probability maps. Discrimination performance and interpretability were tested in external and prospective cohorts, respectively. The correlation between model-generated individual PJI risk and inflammatory biomarkers was assessed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Hip-Net demonstrated strong generalizability across different settings, effectively distinguishing between 5 common types of hip prosthesis failures with an accuracy of 0.904 (95% confidence interval [CI], 0.894 to 0.914) and an area under the receiver operating characteristic curve (AUC) of 0.937 (95% CI, 0.925 to 0.948) in the external cohort. The spatially resolved disease-probability maps for PJI closely aligned with intraoperative and pathological findings. The model-generated individual PJI risk scores exhibited a positive correlation with the C-reactive protein (CRP) level and erythrocyte sedimentation rate (ESR).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Hip-Net provided a clinically applicable strategy for accurately classifying and characterizing multiple etiologies of hip prosthesis failure. Such an approach is highly beneficial for providing interpretable, pathology-aligned probability maps that enhance the understanding of PJI. Its integration into clinical workflows may streamline decision-making and improve patient outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence:<\/jats:title>\n                    <jats:p>\n                      Prognostic\n                      <jats:underline>Level III<\/jats:underline>\n                      . See Instructions for Authors for a complete description of levels of evidence.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.2106\/jbjs.24.01601","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T14:02:19Z","timestamp":1750255339000},"page":"1798-1809","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":4,"title":["A Deep Learning-Based Clinical Classification System for the Differential Diagnosis of Hip Prosthesis Failures Using Radiographs"],"prefix":"10.2106","volume":"107","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8853-4298","authenticated-orcid":false,"given":"Limin","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, 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