Deep Learning-Assisted Ultrasonic Diagnosis and Localizat... | NCT07301086

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

NCT07301086

Sponsor

Ying Jiang

Status

Not yet recruiting

Last Update Posted

Dec 31, 2025Actual

Enrollment

2,000Estimated

Phase

Not provided

Conditions

Testicular Appendix Torsion
Testicular Torsion
Epididymitis

Interventions

Not provided

Countries

China

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Dec 2025

Overall Recruitment Status or Expanded Access Status

Not yet recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jan 2026Estimated

Primary Completion Date

May 2026Estimated

Completion Date

May 2026Estimated

First Submitted Date

Dec 9, 2025

First Submission Date that Met QC Criteria

Dec 23, 2025

First Posted Date

Dec 24, 2025Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Dec 24, 2025

Last Update Posted Date

Dec 31, 2025Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Ying Jiang, resident physician, The Children's Hospital of Zhejiang University School of MedicineSponsor-Investigator

Lead Sponsor

Ying JiangOTHER

Collaborators

Not provided

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Detailed Description

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Yes

Target Follow-Up Duration

1 Day

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

2,000Estimated

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
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

Interventions

Not provided

Outcomes Module

Primary Outcomes

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 Outcomes

Measure	Description	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

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

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

Exclusion Criteria:

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

Accepts Healthy Volunteers

Yes

Sex

Male

Sex/Gender Based

Yes

Sex/Gender Description

Biological male

Minimum Age

1 Minute

Maximum Age

18 Years

Standard Ages

ChildAdult

Study Population

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

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Ying Jiang, Master Degree	Contact	86-19883203100		Jiang_ying@zju.edu.cn
Juntao Jiang, Master Degree	Contact	86-13968107281		juntaojiang@zju.edu.cn

Overall Officials

Name	Affiliation	Role
Jingjing Ye, Phd Degree	The Children's Hospital of Zhejiang University School of Medicine	Study Director

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Children's Hospital of Zhejiang University School of Medicine		Hangzhou	Zhejiang	310000	China

IPD Sharing Statement Module

Plan to Share IPD

Yes

Description

the patient's ultrasound images and baseline data

Types

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.

URL

Not provided

Condition Browse Module

MeSH Terms

ID	Term
D013086	Spermatic Cord Torsion
D004823	Epididymitis

Ancestor Terms

ID	Term
D005832	Genital Diseases, Male
D000091662	Genital Diseases
D000091642	Urogenital Diseases
D052801	Male Urogenital Diseases

Browse Leaves

Not provided

Browse Branches

Not provided