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

Deep Learning-Assisted Ultrasonic Diagnosis and Localization of Testicular Appendix Torsion: A Multicenter Retrospective Validation Study

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Epididymitis; Testicular Torsion; Testicular Appendix Torsion

Detailed Description

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.

• Study Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Ying Jiang, Master Degree; Phone Number: 86-19883203100; Email: Jiang_ying@zju.edu.cn
• Study Contact Backup: Name: Juntao Jiang, Master Degree; Phone Number: 86-13968107281; Email: juntaojiang@zju.edu.cn

Study Locations

• China
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310000
      • Children's Hospital of Zhejiang University School of Medicine
      • Contact: Ying Jiang, Master Degree; Phone Number: 86-19883203100; Email: Jiang_ying@zju.edu.cn
      • Contact: Jingjing Ye, PHD Degree; Phone Number: 86-13868174280
      • Principal Investigator: Ying Jiang, Master Degree

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort
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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Time Frame
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

Collaborators and Investigators

• Sponsor: Ying Jiang
• Investigators: Study Director: Jingjing Ye, Phd Degree, Zhejiang University School of Medicine Children's Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): January 1, 2026
• Primary Completion (Estimated): May 1, 2026
• Study Completion (Estimated): May 1, 2026

Study Registration Dates

• First Submitted: December 9, 2025
• First Submitted That Met QC Criteria: December 23, 2025
• First Posted (Estimated): December 24, 2025

Study Record Updates

• Last Update Posted (Actual): December 31, 2025
• Last Update Submitted That Met QC Criteria: December 24, 2025
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Keywords: Deep Learning; Ultrasonic Diagnosis; Testicular Appendix Torsion
• Additional Relevant MeSH Terms: Urogenital Diseases; Genital Diseases; Genital Diseases, Male; Male Urogenital Diseases; Spermatic Cord Torsion; Epididymitis
• Other Study ID Numbers: CHZhejiangjiangying

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: the patient's ultrasound images and baseline data
• IPD Sharing Time Frame: The IPD and supporting information will be available at 1st Jan,2027, and for one month
• IPD Sharing Access Criteria: Journal editors and reviewers,study protocol,send me email to access it.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No