A Precision-driven Artificial Intelligence Approach for Early Detection of Oral Malignancies

Introduction: Oral cancer remains a primary global health concern, often diagnosed at advanced stages due to delayed detection. Early identification of malignant changes within oral lesions is critical to improving prognosis and survival rates. Artificial intelligence (AI), particularly deep learning, has emerged as a promising tool for image-based diagnostics, offering rapid, non-invasive screening support to clinicians. Aims and Objectives: This study seeks to build a deep learning model that accurately distinguishes between benign and malignant oral lesions by analyzing intraoral images. This approach aims to improve early detection rates, minimize the need for invasive procedures, and enhance accessibility to screening in clinical and remote settings. Methods: InceptionV3, a pre-trained deep learning algorithm, was retrained using a diverse dataset of intraoral images, each labeled based on histopathological findings. The model was optimized to recognize lesion features such as shape, color, texture, size, and location within the oral cavity. The study meets the Standards for Reporting of Diagnostic Accuracy Studies requirements. Results: Performance was evaluated using key metrics: sensitivity (85%), specificity (70%), negative predictive value (NPV) (82.35%), positive predictive value (PPV) (73.9%), false negative rate (FNR) (15%), false positive rate (FPR) (30%), F1-score (79%), and overall accuracy (77.5%). The AI model effectively detected malignant lesions while maintaining moderate-to-high specificity for benign cases. The elevated F1-score reflects a balanced performance between precision and recall, while the overall accuracy supports the model’s viability as a diagnostic tool. Importantly, this model is intended as an adjunct to screening, assisting clinicians, rather than a replacement for histopathological diagnosis, which remains the gold standard. Conclusion: Deep learning-based image analysis offers a non-invasive and efficient adjunct to early screening of oral cancer diagnostics. It enables early detection, reduces the need for biopsies, and enhances screening accessibility. However, it cannot replace histological confirmation. Further clinical validation and optimization are necessary for real-world implementation.