This study aimed to explore the risk factors and potential causes of unilateral classical or idiopathic trigeminal neuralgia (C-ITN) by comparing patients and healthy controls (HCs) with neurovascular compression (NVC) using machine learning (ML).
A total of 84 C-ITN patients and 78 age- and sex-matched HCs were enrolled. We assessed the trigeminal pons angle and identified the compressing vessels and their location and severity. Machine learning was employed to analyze the cisternal segment of the trigeminal nerve (CN V).
Among the C-ITN patients, 53 had NVC on the unaffected side, while 25 HCs exhibited bilateral NVC, and 24 HCs showed unilateral NVC. By comparing the cisternal segment of CN V between C-ITN patients on the affected side and HCs with NVC, we identified the side of NVC, the compressing vessel, and certain texture features as risk factors for C-ITN. Additionally, four texture features differed in the structure of the cisternal segment of CN V between C-ITN patients on the unaffected side and HCs with NVC.
Our findings suggest that the side of NVC, the compressing vessel, and the microstructure of the cisternal segment of CN V are associated with the risk of C-ITN. Furthermore, microstructural changes observed in the cisternal segment of CN V on the unaffected side of C-ITN patients with NVC indicate possible indirect effects on the CN V to some extent.