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.
Parallel
Junior sonographer measurement of DDH
Other interventionParticipants will not receive visual cues from the DeepDDH system. Junior sonographer technicians will offer preliminary interpretations before these are subjected to validation and subsequent review by expert's team.
Senior sonographer measurement of DDH
Other interventionParticipants will not receive visual cues from the DeepDDH system.
Automated annotation of the DDH measurement through deep learning
Other interventionThrough randomization, a subset of the preliminary interpretations will be conducted by AI technology, and the study team will evaluate the degree of divergence between these AI-generated preliminary interpretations and the final interpretations.
AI-assisted junior sonographer meaturement of DDH
Other interventionParticipants will receive visual cues from the DeepDDH system.
Average diagnostic accuracy
It is calculated by dividing the number of preliminary interpretations that are consistent with the expert team's grading by the total number of cases that should be diagnosed.
Average diagnostic sensitivity
It is calculated by dividing the number of true positive cases by the sum of true positive cases and false negative cases.
Average diagnostic specificity
It is calculated by dividing the number of true negative cases by the sum of true negative cases and false positive cases.
Average times of follow-up visits
The average number of follow-up visits for each group is obtained by dividing the total number of follow-up visits for each participating infant within the group by the number of participating infants in that group. The specific calculation of follow-up visits is as follows: For each participating infant, if a false positive diagnosis is made and there are no more subsequent follow-up examination results, the number of follow-up visits is increased by 1; if a false negative diagnosis leads to the end of the follow-up process for the participating infant, then no further follow-up visits are counted.
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