Deep Learning-Assisted Ultrasonic Diagnosis and Localization of Testicular Appendix Torsion
NCT ID: NCT07301086
Last Updated: 2025-12-24
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
2000 participants
OBSERVATIONAL
2026-01-31
2026-05-31
Brief Summary
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In this study, YOLOv11 was adopted as the detection network, which was integrated with the convolutional attention mechanism (Spatial Convolutional Block Attention Module, namely Spatial CBAM) and the self-attention mechanism (e.g., Attention Convolution Mixer, ACMix). The 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 the dataset from the sub-center.Meanwhile, four physicians were assigned to interpret the ultrasound data from the 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 dataset, and comparing the proportion of surgeries predicted by AI with the actual proportion of surgeries, the efficacy of the model was verified.
Detailed Description
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In this study, YOLOv11 was adopted as the detection network, which was integrated with the convolutional attention mechanism (Spatial Convolutional Block Attention Module, namely Spatial CBAM) and the self-attention mechanism (e.g., Attention Convolution Mixer, ACMix). The 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 the dataset from the sub-center.Meanwhile, four physicians were assigned to interpret the ultrasound data from the 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 dataset, and comparing the proportion of surgeries predicted by AI with the actual proportion of surgeries, the efficacy of the model was verified.
Conditions
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Keywords
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Study Design
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COHORT
OTHER
Study Groups
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Testicular Appendix Torsion Group
Patients diagnosed with testicular appendage torsion
No interventions assigned to this group
Testicular Torsion Group
Patients diagnosed with testicular torsion
No interventions assigned to this group
Epididymitis Group
Patients diagnosed with epididymitis
No interventions assigned to this group
Normal Group
Patients with no testicular appendage torsion,testicular torsion,epididymitis,and the scrotum is normal
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
2. Underwent ultrasound examination due to acute scrotal pain (≤ 24 hours)
3. Patients clinically diagnosed with testicular appendage torsion (TAT)
Exclusion Criteria
2. Incomplete clinical data (failure to confirm the diagnosis of testicular appendage torsion \[TAT\])
1 Minute
18 Years
MALE
Yes
Sponsors
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Ying Jiang
OTHER
Responsible Party
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Ying Jiang
resident physician
Principal Investigators
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Jingjing Ye, Phd Degree
Role: STUDY_DIRECTOR
Zhejiang University School of Medicine Children's Hospital
Locations
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Children's Hospital of Zhejiang University School of Medicine
Hangzhou, Zhejiang, China
Countries
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Central Contacts
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Facility Contacts
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Ying Jiang, Master Degree
Role: primary
Jingjing Ye, PHD Degree
Role: backup
Other Identifiers
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CHZhejiangjiangying
Identifier Type: -
Identifier Source: org_study_id