Distinguishing symptomatic and asymptomatic trigeminal nerves through radiomics and deep learning: A microstructural...

The relationship between mild neurovascular conflict (NVC) and trigeminal neuralgia (TN) remains ill-defined, especially as mild NVC is often seen in asymptomatic population without any facial pain. We aim to analyze the trigeminal nerve microstructure using artificial intelligence (AI) to distinguish symptomatic and asymptomatic nerves between idiopathic TN (iTN) and the asymptomatic control group with incidental grade‑1 NVC.

Seventy-eight symptomatic trigeminal nerves with grade-1 NVC in iTN patients, and an asymptomatic control group consisting of Bell's palsy patients free from facial pain (91 grade-1 NVC and 91 grade-0 NVC), were included in the study. Three hundred seventy-eight radiomic features were extracted from the original MRI images and processed with Laplacian-of-Gaussian filters. The dataset was split into 80% training/validation and 20% testing. Nested cross-validation was employed on the training/validation set for feature selection and model optimization. Furthermore, using the same pipeline approach, two customized deep learning models, Dense Atrous Spatial Pyramid Pooling (ASPP) -201 and MobileASPPV2, were classified using the same pipeline approach, incorporating ASPP blocks.

Performance was assessed over ten and five runs for radiomics-based and deep learning-based models. Subspace Discriminant Ensemble Learning (SDEL) attained an accuracy of 78.8%±7.13%, Support Vector Machines (SVM) reached 74.8%±9.2%, and K-nearest neighbors (KNN) achieved 79%±6.55%. Meanwhile, DenseASPP-201 recorded an accuracy of 82.0 ± 8.4%, and MobileASPPV2 achieved 73.2 ± 5.59%.

The AI effectively distinguished symptomatic and asymptomatic nerves with grade‑1 NVC. Further studies are required to fully elucidate the impact of vascular and nonvascular etiologies that may lead to iTN.