Testicular Appendix TorsionNCT07301086
Deep Learning-Assisted Ultrasonic Diagnosis and Localization of Testicular Appendix Torsion
An observational study of Testicular Appendix Torsion and Testicular Torsion, run by Ying Jiang at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 1 minute to 18 years, men onlyHealthy volunteers can take part
- Conditions the study is for
- Testicular Appendix Torsion, Testicular Torsion, Epididymitis
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- Jan 2026to May 2026
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 minute to 18 years
- Men only
- Healthy volunteers accepted
- Child, Adult
Study population: Age ≤ 18 years old，underwent ultrasound examination due to acute scrotal pain (≤ 24 hours)
Who may be able to join
- Age ≤ 18 years old
- Underwent ultrasound examination due to acute scrotal pain (≤ 24 hours)
- Patients clinically diagnosed with testicular appendage torsion (TAT)
Who cannot join
- Poor ultrasound image quality (failure to identify testicular structures)
- Incomplete clinical data (failure to confirm the diagnosis of testicular appendage torsion [TAT])
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Children's Hospital of Zhejiang University School of Medicine
Hangzhou, Zhejiang, 310000
Ying Jiang, Master Degree, 86-19883203100, Jiang_ying@zju.edu.cn
Jingjing Ye, PHD Degree, 86-13868174280
About this study
Official title: 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.
Read the 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.
- Keywords
- Testicular Appendix Torsion, Deep Learning, Ultrasonic Diagnosis
- Medical subject headings
- Spermatic Cord Torsion, Epididymitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: From image input to result generation is expected to be 24 hours
Secondary outcomes (3)
Number of Participants with Acute Scrotal Pain
Measured: From enrollment begin to the end is expected to be 5 months
The accuracy rate of clinicians in diagnosing and localizing testicular appendix torsion
Measured: 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
Measured: 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
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Dec 2025
- Study startJan 2026 (estimated)
- Primary completionMay 2026 (estimated)
- Study completionMay 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Testicular Appendix TorsionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 2,000 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Ying Jiang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Ying Jiang, The Children's Hospital of Zhejiang University School of Medicine
- Investigators
- Jingjing Ye, Phd Degree, Study director, The Children's Hospital of Zhejiang University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07301086
- Sponsor's protocol ID
- CHZhejiangjiangying
- Status last verified
- Dec 2025
- First submitted
- 9 Dec 2025
- Last updated
- 31 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: the patient's ultrasound images and baseline data
Study at a glance
From the registry record, last updated by the sponsor on 31 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning-Assisted Ultrasonic Diagnosis and Localization of Testicular Appendix Torsion: A Multicenter Retrospective Validation Study
- ClinicalTrials.gov ID
- NCT07301086
- Acronym / sponsor ID
- CHZhejiangjiangying
- Keywords
- Testicular Appendix Torsion, Deep Learning, Ultrasonic Diagnosis
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Ying JiangUniversity, hospital or other
- Study start
- Jan 2026 (estimated)
- Primary completion
- May 2026 (estimated)
- Study completion
- May 2026 (estimated)
- First posted
- 24 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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 design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Day
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 1 minute to 18 years
- Sex
- Male only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- Age ≤ 18 years old，underwent ultrasound examination due to acute scrotal pain (≤ 24 hours)
Key inclusion criteria (3 listed)
- Age ≤ 18 years old
- Underwent ultrasound examination due to acute scrotal pain (≤ 24 hours)
- Patients clinically diagnosed with testicular appendage torsion (TAT)
Key exclusion criteria (2 listed)
- Poor ultrasound image quality (failure to identify testicular structures)
- Incomplete clinical data (failure to confirm the diagnosis of testicular appendage torsion [TAT])
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of deep-learning model verify four conditions:testicular appendage torsion;testicular torsion;epididymitis and normal condition
Time frame: From image input to result generation is expected to be 24 hours
accuracy of deep-learning model verify four conditions:testicular appendage torsion;testicular torsion;epididymitis and normal condition
3 secondary outcomes
Number of Participants with Acute Scrotal Pain
Time frame: From enrollment begin to the end is expected to be 5 months
The accuracy rate of clinicians in diagnosing and localizing testicular appendix torsion
Time frame: 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
Time frame: 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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Dec 2025.
- Individual participant dataSharing planned: Yes. the patient's ultrasound images and baseline data
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jingjing Ye, Phd Degree study director
The Children's Hospital of Zhejiang University School of Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Testicular Appendix Torsion and Testicular Torsion, run by Ying Jiang at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 31 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ying Jiang, Master Degree (86-19883203100, Jiang_ying@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of deep-learning model verify four conditions:testicular appendage torsion;testicular torsion;epididymitis and normal condition (time frame: From image input to result generation is expected to be 24 hours). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Dec 2025.