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

NCT ID: NCT06803004

Last Updated: 2025-08-28

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

1789 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-07-18

Study Completion Date

2025-08-20

Brief Summary

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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.

Detailed Description

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1. Participating centers and doctors:

The data in the ultrasound screening sequence database in this part of the study were mainly from Renji Hospital and the Sixth People's Hospital in Shanghai between August 2014 and December 2021. Renji Hospital, the Sixth People's Hospital, and the Pediatric Hospital Affiliated to Fudan University, three top-three hospitals in Shanghai, started Graf ultrasound examination earlier, with an average history of more than 10 years. And they are responsible for providing expert sonographers with more than 5-10 years of DDH ultrasound diagnosis experience, and pediatric orthopedic experts with 5-10 years of DDH diagnosis experience to participate in the study. However, several other primary or remote medical institutions with late DDH ultrasound screening and insufficient diagnostic experience were mainly responsible for providing primary sonographers to participate in the study. Before the study, the sonographers involved in this study will be evaluated uniformly and quantitatively through examination papers.
2. Research process:

One week before the start of the study, the sonographers registered in the study received uniform training of the latest DDH ultrasound diagnosis in the form of PPT, video, literature study, and offline instruction.

For the included cases in the ultrasound screening sequence database, they would appear in different control groups in a random form, such as the AI model, the Expert sonographer group, the primary sonographer group, and the primary sonographer with AI 'aid group. All cases in the ultrasound screening sequence database were stratified and block-randomized into the above four groups (primary, experts, AI-independent, AI-assisted primary).

In the AI-assisted group, each sonographer was asked to choose whether to modify or confirm the diagnosis according to the measurement marks, diagnostic angles and typing results provided by the AI device. However, in the Expert sonographer group and junior sonographer unassisted group, the dedicated research assistant will turn off the AI display function to ensure that no additional information is provided to the sonographer. The consensus of two pediatric orthopedic expert with 5-10 years of experience in DDH ultrasound diagnosis was used as the gold standard. In case of disagreement, a third pediatric expert will evaluate the diagnosis results of DDH. The final consensus was used as the gold standard.

Then, the pediatric orthopedic expert group were given the initial annotations diagnosis results of DDH in the above four groups, including diagnostic images, diagnostic measurement marks, diagnostic angles and diagnostic types. And by reviewing the initial annotations, selecting "confirm" or "modify" the initial annotations, the final annotations are made again for those who need to be modified, and the final report results are obtained.

Finally, the operation results of the above different groups were summarized and analyzed by independent research assistants, including α Angle, β Angle, typing results, and the specific follow-up experience of the case including follow-up times, diagnosis time, Bang's index, proportion of studies the annotation is changed, proportion of studies the DDH type is changed in final report, and mean change in alpha angle between preliminary and final report.

Conditions

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Hip Dysplasia, Developmental

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

DOUBLE

Investigators Outcome Assessors

Study Groups

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Junior Sonographer Annotation

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.

Group Type ACTIVE_COMPARATOR

Junior sonographer measurement of DDH

Intervention Type 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.

Senior Sonographer Annotation

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.

Group Type ACTIVE_COMPARATOR

Senior sonographer measurement of DDH

Intervention Type 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.

DeepDDH system Annotation

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.

Group Type EXPERIMENTAL

Automated annotation of the DDH measurement through deep learning

Intervention Type 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-assist Junior Sonographer Annotation

Participants will receive visual cues from the DeepDDH system.

Group Type EXPERIMENTAL

AI-assisted junior sonographer measurement of DDH

Intervention Type OTHER

Participants will receive visual cues from the DeepDDH system.

Interventions

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Junior sonographer measurement of DDH

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.

Intervention Type OTHER

Senior sonographer measurement of DDH

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.

Intervention Type OTHER

Automated annotation of the DDH measurement through deep learning

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.

Intervention Type OTHER

AI-assisted junior sonographer measurement of DDH

Participants will receive visual cues from the DeepDDH system.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

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

Exclusion Criteria

* 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.
Minimum Eligible Age

28 Days

Maximum Eligible Age

6 Months

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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RenJi Hospital

OTHER

Sponsor Role lead

Responsible Party

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Lixin Jiang

Director of Department of ultrasound in medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine

Shanghai, Shanghai Municipality, China

Site Status

Countries

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China

Other Identifiers

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LY2025-004-C

Identifier Type: -

Identifier Source: org_study_id

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