Bridging Imaging and Molecular Biomarkers in Trigeminal Neuralgia: Toward Precision Diagnosis and Prognostication in Neuropathic Pain

Despite advances in imaging and clinical assessment, current diagnostic paradigms lack precision needed to subcategorize disease or predict therapeutic response with accuracy. This review proposes precision neuropathic pain diagnostics through the combination of artificial intelligence-guided imaging and molecular biomarker identification, using the use-case of Trigeminal neuralgia (TN), a refractory craniofacial pain disorder characterized by paroxysmal, one-sided facial pain and variable treatment response. We use trigeminal neuralgia as a case example to explore how deep learning, advanced imaging, and molecular profiling can work together to improve the diagnosis and treatment of neuropathic pain. First, the current paper reviews contemporary advances in deep learning for neuroimaging, particularly convolutional neural networks and U-Net architectures that have enabled automatic segmentation of the trigeminal nerve and paved the way for radiomic measurement of neurovascular compression. Such advances optimize the objectivity of surgery stratification and help to distinguish classical from idiopathic Trigeminal Neuralgia. In parallel, proteomic profiling of cerebrospinal fluid and plasma has revealed TN-specific molecular signatures, including upregulation of inflammatory, stress-related, and axonal damage markers that are different from those of similar disorders such as multiple sclerosis. Both markers not only have implications for disease pathobiology but also for identifying new therapeutic targets. The current study proposes a multimodal data integration platform combining imaging and molecular phenotypes using machine learning and multi-omics platforms. The integration permits mechanistic subtyping and predictive modeling of treatment response, with potential applications expanding to diabetic neuropathy and complex regional pain syndrome. Clinical deployment challenges, such as heterogeneity of data, ambiguity regarding regulation, and ethical risk, are addressed along with near-term solutions such as federated learning and interoperable biomarker registries. At the intersection of neurosurgery, radiology, and computational science, Trigeminal Neuralgia offers a scalable model for precision pain medicine, transforming care through mechanism-based classification and patient-stratified interventions.