Epididymitis, Testicular Appendix Torsion, Testicular Torsion
Conditions
Keywords
Testicular Appendix Torsion, Deep Learning, Ultrasonic Diagnosis
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
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.
Interventions
None listed
Sponsors
Ying Jiang
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
MALE
Age
1 Minutes to 18 Years
Healthy volunteers
Yes
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 \[TAT\])
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 | accuracy of deep-learning model verify four conditions:testicular appendage torsion;testicular torsion;epididymitis and normal condition |
Secondary
| 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 |
Countries
China
Contacts
Primary ContactYing Jiang, Master Degree
Backup ContactJuntao Jiang, Master Degree
Outcome results
None listed