Clinical trial · Observational
Application and Evaluation of Vision-LSTM Modeling in Diagnostic Ultrasound Imaging of TI-RADS Category 4b Thyroid Nodules
Application and Evaluation of Vision-LSTM Model in Diagnostic Ultrasound Imaging of TI-RADS Class 4b Thyroid Nodules
NCT07496684CI-TRIAL-00121680completedClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The aim of this study was to evaluate the performance of artificial intelligence (AI) technology in the diagnosis of thyroid nodules, specifically in the field of ultrasound image analysis. It focuses on the accuracy and clinical feasibility of the AI system based on the Vision-LSTM model in the diagnosis of TI-RADS category 4b thyroid nodules.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Thyroid Cancer | Malignant Thyroid Gland Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (2)
- label
- maligant
- description
- patients with maligant thyroid masses who underwent biopsy and/or surgical resection.
- label
- benign
- description
- patients with benign thyroid masses who underwent biopsy and/or surgical resection.
Primary outcomes (1)
- measure
- Accuracy of diagnostic models
- timeFrame
- Immediately evaluated after the diagnostic model was built
- description
- The study collected ultrasound imaging data from 401 cases of TI-RADS 4b thyroid nodules at our hospital and used this data to train and validate the Vision-LSTM model. The diagnostic results of the AI model were compared with those of junior and senior clinicians to evaluate its performance in terms of diagnostic accuracy and stability; model performance was quantified using metrics such as the area under the curve (AUC) and the precision-recall curve (PR curve).
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 20 Years
- Maximum age
- 78 Years
Show eligibility criteria text
Inclusion Criteria:(1) Patients with thyroid nodules visible on ultrasound who underwent biopsy and/or surgical resection. (2) Diagnosed as TI-RADS category 4b on the basis of preoperative ultrasound images by two sonographers with more than 5 years of experience in thyroid ultrasound diagnosis. (3) All nodules underwent puncture biopsy or surgery to obtain pathologic results. \- Exclusion Criteria:(1)The quality of the patient's ultrasound images was poor. (2) The patient has incomplete clinical and imaging data. (3) The patient has had thyroid surgery or other treatment. \-
References
Publications (0)
Data not yet available
No reference posted for this study.