Diagnostic Efficacy Study of AI System in Screening Infants With Developmental Dysplasia of the Hip

NCT06803004 | Completed DMC

• 1 other identifier: LY2025-004-C
• Study Type: interventional
• Target: 1,789
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Hip Dysplasia, Developmental

Outcome Measures

Primary Outcomes (1)

• 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.

  up to 4 weeks

Secondary Outcomes (8)

• Average diagnostic sensitivity

  up to 4 weeks

• Average diagnostic specificity

  up to 4 weeks

• Average times of follow-up visits

  up to 4 weeks

• Diagnosis time

  up to 4 weeks

• Bang's index

  up to 4 weeks

Study Arms (4)

Junior Sonographer Annotation

ACTIVE COMPARATOR

Participants 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.

Other: Junior sonographer measurement of DDH

Senior Sonographer Annotation

ACTIVE COMPARATOR

Participants will not receive visual cues from the DeepDDH system. Senior sonographer technicians will offer preliminary interpretations before these are subjected to validation and subsequent review by expert's team.

Other: Senior sonographer measurement of DDH

DeepDDH system Annotation

EXPERIMENTAL

Through 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.

Other: Automated annotation of the DDH measurement through deep learning

DeepDDH-assist Junior Sonographer Annotation

EXPERIMENTAL

Participants will receive visual cues from the DeepDDH system.

Other: AI-assisted junior sonographer measurement of DDH

Interventions

Junior sonographer measurement of DDH OTHER

Participants 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.

Junior Sonographer Annotation

Senior sonographer measurement of DDH OTHER

Participants will not receive visual cues from the DeepDDH system. Senior sonographer technicians will offer preliminary interpretations before these are subjected to validation and subsequent review by expert's team.

Senior Sonographer Annotation

Automated annotation of the DDH measurement through deep learning OTHER

Through 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.

DeepDDH system Annotation

AI-assisted junior sonographer measurement of DDH OTHER

Participants will receive visual cues from the DeepDDH system.

DeepDDH-assist Junior Sonographer Annotation

Eligibility Criteria

• Age: 28 Days - 6 Months
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)

You may qualify if:

• Infants underwent DDH ultrasound examinations.
• Infants aged 28 days to 6 months.

You may not qualify if:

• Infants with lacking or incomplete ultrasound images.
• Infants with poor image quality, including non-compliance with anatomical identification and usability check.
• Infants with hip dysplasia caused by other diseases.

Sponsors & Collaborators

• RenJi Hospital: lead

Study Sites (1)

Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine

Shanghai, Shanghai Municipality, 200127, China

Location

MeSH Terms

Conditions

Developmental Dysplasia of the Hip

Condition Hierarchy (Ancestors)

Hip Dislocation; Joint Dislocations; Joint Diseases; Musculoskeletal Diseases; Musculoskeletal Abnormalities; Congenital Abnormalities; Congenital, Hereditary, and Neonatal Diseases and Abnormalities

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: INVESTIGATOR, OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director of Department of ultrasound in medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine

Study Record Dates

• First Submitted: January 20, 2025
• First Posted: January 31, 2025
• Study Start: July 18, 2025
• Primary Completion: August 1, 2025
• Study Completion: August 20, 2025
• Last Updated: August 28, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, CSR, ANALYTIC CODE

Locations

CN (1)