Deep Learning-Assisted Ultrasonic Diagnosis and Localization of Testicular Appendix Torsion

NCT07301086 | Not Yet Recruiting

• 1 other identifier: CHZhejiangjiangying
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

Ultrasound data were both retrospectively and prospectively collected from the primary center and six other sub-centers. Combined with clinical diagnostic outcomes, the data labeling was completed by physicians with extensive clinical experience. In this study, ConvNeXtV2 was used as the classification network and YOLOv12 was adopted as the detection network.The retrospective dataset from the primary center was split into training, validation, and test subsets, on which the model was trained, validated, and tested respectively; additional validation was conducted on both retrospective and prospective datasets from the primary center and sub-centers.Meanwhile, four physicians were assigned to interpret the ultrasound data from the retrospective and prospective datasets from the primary center and sub-centers using two diagnostic methods-independent diagnosis and artificial intelligence (AI)-assisted diagnosis-and the diagnostic accuracy of these two approaches was further compared.By collecting and learning the treatment methods of patients in the primary center training set, predicting the treatment methods of patients in the sub-center datasets, and comparing the proportion of surgeries predicted by AI with the actual proportion of surgeries, the efficacy of the model was verified.

Conditions

Testicular Appendix Torsion; Testicular Torsion; Epididymitis

Keywords

Testicular Appendix Torsion; Deep Learning; Ultrasonic Diagnosis

Outcome Measures

Primary Outcomes (1)

• Accuracy of deep-learning model verify four conditions:testicular appendage torsion;testicular torsion;epididymitis and normal condition

  accuracy of deep-learning model verify four conditions:testicular appendage torsion;testicular torsion;epididymitis and normal condition

  From image input to result generation is expected to be 24 hours

Secondary Outcomes (3)

• Number of Participants with Acute Scrotal Pain

  From enrollment begin to the end is expected to be 5 months

• The accuracy rate of clinicians in diagnosing and localizing testicular appendix torsion

  From the begin of Clinicians diagnose and locate to the end is expected to be 15 days

• The accuracy rate of the Deep learning model in predicting the treatment modality for testicular appendix torsion,conservative treatment or surgery

  From the begin of the prediction of treatment for testicular appendix torsion by Deep learning model to the end is expected to be 24 hours

Study Arms (4)

Testicular Appendix Torsion Group

Patients diagnosed with testicular appendage torsion

Testicular Torsion Group

Patients diagnosed with testicular torsion

Epididymitis Group

Patients diagnosed with epididymitis

Normal Group

Patients with no testicular appendage torsion,testicular torsion,epididymitis,and the scrotum is normal

Eligibility Criteria

• Age: 1 Minute - 18 Years
• Gender Eligibility Details: Biological male
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Age ≤ 18 years old，underwent ultrasound examination due to acute scrotal pain (≤ 24 hours)

You may qualify if:

• Age ≤ 18 years old
• Underwent ultrasound examination due to acute scrotal pain (≤ 24 hours)
• Patients clinically diagnosed with testicular appendage torsion (TAT)

You may not qualify if:

• Poor ultrasound image quality (failure to identify testicular structures)
• Incomplete clinical data (failure to confirm the diagnosis of testicular appendage torsion \[TAT\])

Sponsors & Collaborators

• Ying Jiang: lead

Study Sites (1)

Children's Hospital of Zhejiang University School of Medicine

Hangzhou, Zhejiang, 310000, China

Location

MeSH Terms

Conditions

Spermatic Cord Torsion; Epididymitis

Condition Hierarchy (Ancestors)

Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Male Urogenital Diseases

Study Officials

• Jingjing Ye, Phd Degree

  Zhejiang University School of Medicine Children's Hospital

  STUDY DIRECTOR

Central Study Contacts

Ying Jiang, Master Degree

CONTACT

86-19883203100; Jiang_ying@zju.edu.cn

Juntao Jiang, Master Degree

CONTACT

86-13968107281; juntaojiang@zju.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: resident physician

Study Record Dates

• First Submitted: December 9, 2025
• First Posted: December 24, 2025
• Study Start: January 1, 2026
• Primary Completion: May 1, 2026
• Study Completion: May 1, 2026
• Last Updated: December 31, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: The IPD and supporting information will be available at 1st Jan,2027, and for one month
• Access Criteria: Journal editors and reviewers,study protocol,send me email to access it.

Locations

CN (1)