BACKGROUNDHead and neck cancers, constituting 3-5% of all cancer cases, often require surgical resection for optimal outcomes. Achieving complete resection (R0) is crucial, but current methods, relying on white light endoscopy and microscopy, have limitations. Hyperspectral imaging (HSI) offers potential benefits by capturing detailed spectral information beyond human vision.MATERIAL AND METHODSThis study enrolled 32 patients with head and neck squamous cell carcinoma (HNSCC). Following surgical resection specimens underwent ex vivo HSI imaging. Annotated regions were utilized to train a Convolutional Neural Network (CNN) and Graph Neural Network (GNN). Imaging parameters were carefully optimized for efficiency.RESULTSOur HSI imaging setup required around 12 min per measurement and demonstrated feasibility with promising accuracy. The combination of HSI and artificial intelligence (AI) achieved an 86% accuracy in predicting tumor tissue. Challenges included data volume and extended capture times.CONCLUSIONHyperspectral imaging, complemented by AI, shows promise in enhancing tissue differentiation for HNSCC. The study envisions real-time integration of HSI into surgery for margin assessment. Challenges such as data volume and capture times warrant further exploration, emphasizing the need for ongoing investigations to refine clinical applications.