ArticleBMC medical imaging2024
Application research on the diagnosis of classic trigeminal neuralgia based on VB-Net technology and radiomics.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.BMC musculoskeletal disorders · 2025Article
- Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment.Frontiers in neurology · 2024Article
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9 authors.
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Abstract
backgroundThis study aims to utilize the deep learning method of VB-Net to locate and segment the trigeminal nerve, and employ radiomics methods to distinguish between CTN patients and healthy individuals.
methodsA total of 165 CTN patients and 175 healthy controls, matched for gender and age, were recruited. All subjects underwent magnetic resonance scans. VB-Net was used to locate and segment the bilateral trigeminal nerve of all subjects, followed by the application of radiomics methods for feature extraction, dimensionality reduction, feature selection, model construction, and model evaluation.
resultsOn the test set for trigeminal nerve segmentation, our segmentation parameters are as follows: the mean Dice Similarity Coefficient (mDCS) is 0.74, the Average Symmetric Surface Distance (ASSD) is 0.64 mm, and the Hausdorff Distance (HD) is 3.34 mm, which are within the acceptable range. Analysis of CTN patients and healthy controls identified 12 features with larger weights, and there was a statistically significant difference in Rad_score between the two groups (p < 0.05). The Area Under the Curve (AUC) values for the three models (Gradient Boosting Decision Tree, Gaussian Process, and Random Forest) are 0.90, 0.87, and 0.86, respectively. After testing with DeLong and McNemar methods, these three models all exhibit good performance in distinguishing CTN from normal individuals.
conclusionsRadiomics can aid in the clinical diagnosis of CTN, and it is a more objective approach. It serves as a reliable neurobiological indicator for the clinical diagnosis of CTN and the assessment of changes in the trigeminal nerve in patients with CTN.
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