Garden classification of femoral neck fracture using deep-learning algorithm.

ABSTRACT: The Garden classification, based on X-ray interpretation and established over 50 years ago, remains the standard clinical classification system for femoral neck fractures (FNFs). Yet, this classification has a high interobserver variability of 70%. We sought to develop a deep-learning algorithm capable of accurately predicting FNF types, using only X-ray images, with performance comparable to that of computed tomography (CT). We retrospectively collected data from 1,588 patients who underwent X-ray and 3D-CT scans and were diagnosed with femoral neck fractures at Asan Medical Center. The input X-ray dataset consisted of paired X-ray images of the hip, with anteroposterior (AP) and lateral views. Using 3D-CT as the reference standard, patients were labeled as Garden types I (n = 378, 23.8%)

SUBMITTER: Yang J 

PROVIDER: S-EPMC12708817 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature