Artificial Intelligence-Based Automatic Screening System for Hip Dysplasia

Abstract

To develop an artificial intelligence-based radiographic screening system for hip dysplasia and to evaluate its diagnostic performance and clinical utility. Seventy-three patients with hip dysplasia who underwent unilateral periacetabular osteotomy and 29 asymptomatic volunteers were included. Radiographs from patients with hip dysplasia were divided into training (n = 40), validation (n = 7), and testing (n = 26) datasets, whereas radiographs from volunteers were used exclusively for testing. The artificial intelligence system detects and segments anatomical landmarks on pelvic radiographs, quantifies parameters reflecting anterior, posterior, and lateral acetabular coverage, and determines whether a patient has hip dysplasia based on these measurements. It comprises two deep learning models: DeepLabCut and U-Net, which can effectively learn anatomical structures from relatively small training data. Ground-truth measurements were obtained from repeated assessments by a hip surgeon and a non-hip surgeon, and hip dysplasia status was independently determined by two hip surgeons in blinded assessments. Agreement with the ground-truth was assessed using intraclass correlation coefficients, and screening performance was compared with that of general orthopaedic residents using the test images. Agreement with the ground-truth was good to excellent [intraclass correlation coefficient: 0.70–0.95]. The system showed the best overall screening performance (sensitivity, 0.96 vs. 0.44–0.97; specificity, 0.92 vs. 0.16–0.92; area under the curve, 0.94 vs. 0.57–0.76; F-measure, 0.96 vs. 0.60–0.81) compared with the residents. Our artificial intelligence-based screening system may serve as a primary care-oriented radiographic screening and referral-support tool for clinicians, including non-specialists, despite being trained on a limited dataset.