Early Detection of Oral Cancer Using Machine Learning in Artificial Intelligence and Electronic Tongue: A Systematic Review
Abstract
This systematic review examines the role of artificial intelligence (AI) and electronic tongue (etongue) technology in the early detection of oral cancer. A comprehensive search of PubMed, Scopus, and Web of Science databases identified 500 articles, of which 20 met the inclusion criteria and were included in the review. The included studies encompassed diverse methodologies, AI models, and e-tongue technologies, with a total of 5,000 participants across various populations. AI-driven models, particularly those utilizing deep learning algorithms, demonstrated high sensitivity (>85%) and specificity (>80%) in detecting oral cancer biomarkers. E-tongue technologies, such as mass spectrometry and optical sensors, contributed to enhanced diagnostic accuracy, with area under the curve (AUC) values exceeding 0.85 in several studies. While promising, challenges such as study heterogeneity, validation in large-scale trials, and implementation barriers require further attention. The findings highlight the transformative potential of AI and e-tongue technology in revolutionizing oral cancer screening and management, with implications for improving patient outcomes and reducing healthcare costs. Future research should focus on standardization, validation, and real-world implementation to harness the full benefits of these innovative approaches in clinical practice.
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