Enhancing Oral Health Diagnostics With Hyperspectral Imaging and Computer Vision: Clinical Dataset Study

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Background: Diseases of the oral cavity, including oral squamous cell carcinoma, pose major challenges to health care worldwide due to their late diagnosis and complicated differentiation of oral tissues. The combination of endoscopic hyperspectral imaging (HSI) and deep learning (DL) models offers a promising approach to the demand for modern, noninvasive tissue diagnostics. This study presents a large-scale in vivo dataset designed to support DL-based segmentation and classification of healthy oral tissues. Objective: This study aimed to develop a comprehensive, annotated endoscopic HSI dataset of the oral cavity and to demonstrate automated, reliable differentiation of intraoral tissue structures by integrating endoscopic HSI with advanced machine learning methods. Methods: A total of 226 participants (166 women [73.5%], 60 men [26.5%], aged 24-87 years) were examined using an endoscopic HSI system, capturing spectral data in the range of 500 to 1000 nm. Oral structures in red, green, and blue and HSI scans were annotated using RectLabel Pro (by Ryo Kawamura). DeepLabv3 (Google Research) with a ResNet-50 backbone was adapted for endoscopic HSI segmentation. The model was trained for 50 epochs on 70% of the dataset, with 30% for evaluation. Performance metrics (precision, recall, and F1-score) confirmed its efficacy in distinguishing oral tissue types. Results: DeepLabv3 (ResNet-101) and U-Net (EfficientNet-B0/ResNet-50) achieved the highest overall F1-scores of 0.857 and 0.84, respectively, particularly excelling in segmenting the mucosa (0.915), retractor (0.94), tooth (0.90), and palate (0.90). Variability analysis confirmed high spectral diversity across tissue classes, supporting the dataset's complexity and authenticity for realistic clinical conditions. Conclusions: The presented dataset addresses a key gap in oral health imaging by developing and validating robust DL algorithms for endoscopic HSI data. It enables accurate classification of oral tissue and paves the way for future applications in individualized noninvasive pathological tissue analysis, early cancer detection, and intraoperative diagnostics of oral diseases.

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