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

Blinded Randomized Control Trail of Artificial Intelligence-Assisted Ultrasound Screening for Neonatal Hip Dysplasia in a Clinical Cohort

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Hip Dysplasia, Developmental
• Intervention / Treatment: Other: Junior sonographer measurement of DDH; Other: Senior sonographer measurement of DDH; Other: Automated annotation of the DDH measurement through deep learning; Other: AI-assisted junior sonographer meaturement of DDH

Detailed Description

1. Participating centers and doctors:

   The data in the ultrasound screening sequence database in this part of the study were mainly from Renji Hospital, Shanghai Jiao Tong University School of Medicine and the Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine. Renji Hospital, the Sixth People's Hospita, 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. Each unit can perform Graf ultrasound examination maturely, and has good quality control and management. The technology is relatively mature, and they are responsible for providing 6 expert sonographers with more than 5-10 years of DDH ultrasound diagnosis experience, more than 600 DDH ultrasound operations, and 1 pediatric orthopedic expert 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 8 primary sonographers (trained residents who performed 20 DDH ultrasound operations) to participate in the study. The other 4 primary sonographers who participated in the study were provided by the Sixth People's Hospital and the Pediatric Hospital Affiliated to Fudan University, so a total of 12 primary sonographers participated in the study. Before the study, the sonographers involved in this study will be evaluated uniformly and quantitatively through online examination papers.

2. Research process:

One week before the start of the randomized diagnostic trial study, which prospectively reevaluated the retrospective data set of the ultrasound screening sequence database, the sonographers registered in the study received uniform training in 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 (composed of 6 ultrasound experts), the primary sonographer group (composed of 6 primary sonographers), and the primary sonographer (composed of 6 primary sonographers) + AI-assisted group. All the image data in the ultrasound screening sequence database were independently divided into measurement points and measurement lines by each group, and the final diagnosis of α Angle, β Angle and classification results were given by the system.

The random occurrence of cases in the ultrasound screening sequence database has certain rules: (1) According to the date of ultrasound examination, the 2152 ultrasound screenings were divided into 848 days. (2) Block randomization was applied to the 848-day study, using a computer-generated sequence of random numbers, with randomization performed in blocks of four or eight. That is, the cases examined for the first time in each day were divided into one group (AI group, advanced ultrasound expert group, primary sonographer group, AI-assisted primary sonographer group), and the cases for follow-up examination were divided according to the first examination group. Randomization was repeated within each group according to block randomization of 6 or 12 block sizes.

When non-first subsequent follow-up cases were randomly presented, the results of the previous examination were included as a reference. In the AI-assisted group, each sonographer was asked to choose whether to modify or confirm the diagnosis according to the measurement points, diagnostic angles and typing results provided by the AI device. However, in the advanced ultrasound expert group, 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 ultrasound experts with 10 years of experience in DDH ultrasound diagnosis was used as the gold standard. In case of disagreement, a third pediatric expert with 5-10 years of experience in DDH diagnosis and treatment was used to evaluate the diagnosis and treatment of DDH. The final consensus was used as the gold standard.

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 and follow-up time).

• Study Type: Interventional
• Enrollment (Estimated): 1976
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Lixin Jiang, PhD; Phone Number: +86 18930173496; Email: jinger_28@sina.com
• Study Contact Backup: Name: Mengyao Liu, PhD; Phone Number: +86 17521618149; Email: mengyao_liu08@163.com

Study Locations

• China
  • Shanghai
    • Shanghai, Shanghai, China, 200127
      • Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine
      • Contact: Hui Cao; Phone Number: +86 021-6838-3364; Email: rjllb3364@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Double

Number of Arms

4

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Active Comparator: 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.	Other: 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.; Other Names: Junior Sonographer Annotation
Active Comparator: Senior Sonographer Annotation; Participants will not receive visual cues from the DeepDDH system.	Other: Senior sonographer measurement of DDH; Participants will not receive visual cues from the DeepDDH system.; Other Names: Senior Sonographer Annotation
Experimental: 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.	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.; Other Names: DeepDDH system Annotation
Experimental: DeepDDH-assist Junior Sonographer Annotation; Participants will receive visual cues from the DeepDDH system.	Other: AI-assisted junior sonographer meaturement of DDH; Participants will receive visual cues from the DeepDDH system.; Other Names: DeepDDH-assist Junior Sonographer Annotation

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	30 Minutes
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.	30 Minutes
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.	30 Minutes

Collaborators and Investigators

• Sponsor: RenJi Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): January 15, 2025
• Primary Completion (Estimated): March 30, 2025
• Study Completion (Estimated): April 30, 2025

Study Registration Dates

• First Submitted: January 3, 2025
• First Submitted That Met QC Criteria: January 3, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 3, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Musculoskeletal Diseases; Wounds and Injuries; Joint Diseases; Musculoskeletal Abnormalities; Congenital Abnormalities; Hip Injuries; Joint Dislocations; Developmental Dysplasia of the Hip; Hip Dislocation, Congenital; Hip Dislocation
• Other Study ID Numbers: KY2021-286-B

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The clinical demographic information of the subjects was recorded through the Case Record Form, and the data was collected and managed electronically through the ResMan platform.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No