# Recent advance in early oral lesion diagnosis: the application of artificial intelligence-assisted endoscopy

## TL;DR

AI-assisted endoscopy improves early detection of oral cancer by automating lesion diagnosis, potentially enhancing outcomes in underserved areas.

## Contribution

The paper highlights AI's role in overcoming limitations of traditional endoscopic diagnosis through deep learning models like Mask R-CNN and U-Net.

## Key findings

- AI-assisted endoscopy improves lesion detection accuracy and reduces human error.
- Transfer learning and data augmentation help address overfitting in AI models.
- Ethical and clinical validation challenges remain for widespread AI adoption in endoscopy.

## Abstract

Oral squamous cell carcinoma (OSCC) is a globally prevalent malignancy with high mortality. Early detection is crucial, yet traditional diagnostic methods, including biopsies and imaging techniques like CT and MRI, face limitations in identifying small or superficial lesions. Endoscopic techniques, such as White Light Imaging, Narrow Band Imaging, and Autofluorescence Imaging, enhance visualization of mucosal abnormalities, but their accuracy depends on operator expertise. Recent advancements in artificial intelligence (AI) are transforming endoscopic diagnosis by enabling automated lesion detection, segmentation, and classification through deep learning models like Mask R-CNN and U-Net. These AI-driven approaches improve diagnostic precision, reduce human error, and facilitate early intervention, particularly in resource-limited settings. Challenges persist, including the need for standardized datasets, robust preprocessing methods, and strategies to address overfitting in AI models. Techniques such as transfer learning, data augmentation, and multitask learning are employed to overcome these limitations. AI-assisted endoscopy holds promise for early detection, improved treatment outcomes, and enhanced accessibility, particularly in underserved regions. However, ethical concerns, data privacy, and the necessity for clinical validation remain critical. Future research should prioritize refining AI methodologies and integrating them into clinical workflows to optimize the early diagnosis and management of OSCC, thereby improving patient outcomes and reducing global disease burden.