To ascertain the efficacy of the DeepDDH system, a deep learning framework, in enhancing diagnostic accuracy and curtailing follow-up intervals for infants undergoing screening for developmental dysplasia of the hip (DDH), the researchers are executing a blinded, randomized controlled trial. This trial juxtaposes AI-only and AI-assisted assessments of DDH against sonographer interpretations across various proficiency levels in the preliminary analysis of ultrasound images.
Infants aged 28 days to 6 months who underwent DDH ultrasound examination in Renji Hospital, Shanghai Jiaotong University School of Medicine and the Sixth People's Hospital, Shanghai Jiaotong University School of Medicine between August 2014 and December 2021
Patients lacking or incomplete ultrasound images;
Patients with poor image quality and unusable images after assessment, including non-compliance with Anatomical identification (Checklist I consists of seven anatomical structures: 1. Chondro-osseous border, 2. Femoral head, 3. Synovial fold, 4. Joint capsule and perichondrium, 5. Labrum, 6. Cartilagineous roof, 7. Bony roof) and Usability check (Checklist II includes three anatomical landmarks: 1. Lower limb of the os ilium, 2. Parallel middle plane, and 3. Labrum);
Infants with hip dysplasia caused by other diseases such as cerebral palsy, joint contracture, suppurative coxitis, etc., or with other hip diseases and limb deformities.