Deep Learning-Assisted Ultrasonic Diagnosis and Localization of Testicular Appendix Torsion
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Testicular Appendix Torsion, Testicular Torsion, Epididymitis. Basic parameters: 1 Minute — 18 years · Male.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Deep Learning-Assisted Ultrasonic Diagnosis and Localization of Testicular Appendix Torsion: A Multicenter Retrospective Validation Study
Overview
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.
Primary outcome measures
- 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]
Secondary outcome measures (3)
- 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]
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 \[TAT\])
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Children's Hospital of Zhejiang University School of Medicine — Hangzhou
Identifiers
NCT: NCT07301086 · CHZhejiangjiangying