Efficacy Study of DeepDDH System in Screening Infants with Developmental Dysplasia of the Hip (DDH)

NCT06765525 | Not Yet Recruiting DMC

• 1 other identifier: KY2021-286-B
• Study Type: interventional
• Target: 1,976
• 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.

  30 Minutes

Secondary Outcomes (3)

• Average diagnostic sensitivity

  30 Minutes

• Average diagnostic specificity

  30 Minutes

• Average times of follow-up visits

  30 Minutes

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.

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 meaturement 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.

Also known as: Junior Sonographer Annotation

Junior Sonographer Annotation

Senior sonographer measurement of DDH OTHER

Participants will not receive visual cues from the DeepDDH system.

Also known as: Senior Sonographer Annotation

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.

Also known as: DeepDDH system Annotation

DeepDDH system Annotation

AI-assisted junior sonographer meaturement of DDH OTHER

Participants will receive visual cues from the DeepDDH system.

Also known as: DeepDDH-assist Junior Sonographer Annotation

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 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

You may not qualify if:

• 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.

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

Central Study Contacts

Lixin Jiang, PhD

CONTACT

+86 18930173496; jinger_28@sina.com

Mengyao Liu, PhD

CONTACT

+86 17521618149; mengyao_liu08@163.com

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 3, 2025
• First Posted: January 9, 2025
• Study Start: January 15, 2025
• Primary Completion: March 30, 2025
• Study Completion: April 30, 2025
• Last Updated: January 9, 2025

Record last verified: 2025-01

Data Sharing

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

Locations

CN (1)