Early Detection of Oral Squamous Cell Carcinoma by Image Analysis using Artificial Intelligence and Nano-diagnostics

Background: Artificial Intelligence (AI) combined with nanotechnology could detect oral cancer development in an earlier stage by using various advanced techniques such as biosensors, Raman scattering, bio-imaging, smartphones, and AI applications. Objective: This study aimed to review the latest developments in sophisticated early oral cancer diagnosis using AI techniques combined with nanotechnologies such as Raman scattering and Oral Squamous Cell Carcinoma (OSCC) imaging models. Methods: Machine learning includes Gabor filters, Resnet 50 for feature extraction, and nanotechnologies such as Raman scattering. An AI smartphone-based image module helped to detect oral cancers such as high-risk OSCC. Results: AI systems enhance oral cancer identification. Nano-biosensors and Raman scattering aid in precise detection. AI models, like CNNs, accurately classify oral lesions. Integrating AI, IoT, and smartphones enables remote screening in underserved communities. Discussion: Artificial intelligence enhances ML and DL accuracy for oral cancer diagnosis. The hybrid Gabor filter, Resnet 50, and nano-based methods impact treatment. Conclusion: Artificial intelligence and nano-based oral cancer detection, using ML and DL, offer precise diagnosis. These technological advancements enable early detection and improve treatment outcomes.