Progress in Early Diagnosis of Oral Squamous Cell Carcinoma: From Cytology to AI-Based Imaging Analysis

Abstract

Oral squamous cell carcinoma (OSCC) is the most common type of oral malignancy, and its five-year survival rate is highly dependent on the timing of diagnosis. In recent years, advances in cytology, optical imaging technologies, and artificial intelligence (AI) based image analysis have enriched the available strategies for early detection of OSCC. This review summarizes traditional diagnostic approaches, including oral brush cytology, liquid-based cytology, tissue biopsy, and immunohistochemistry. It further highlights recent developments in optical techniques such as narrow-band imaging and autofluorescence-based methods, and provides an overview of emerging deep learning driven automated recognition technologies. Current evidence suggests that future OSCC early-diagnosis systems will evolve toward multimodal, intelligent, and non-invasive frameworks, offering greater potential for improving clinical outcomes.