A Novel Method for the Detection of Oral Cancers: Deep Learning

Abstract

Abstract Background:Oral cancers are among the most common cancers worldwide. Deep learning (DL) represents a technology that emulates human characteristics through programmed algorithms. In our study, oral cancer photographs were presented to dental professionals and artifical intelligence (AI) systems for diagnosis. Materials and Methods: Verified photographs of oral lesions and healthy oral tissues were obtained from scientific publications, internet search engines, and personal archives. AI systems were trained using DL networks. The trained models were tested on a test set of photographs that were not used for training. The objective was to detect and categorize the lesions. Dental professionals were also asked to categorize the lesions based on the test set photographs. The performances of AI systems and dental professionals were compared. Results:The survey included responses from 154 dentists, with an average score of 0.871 ± 0.110. Among dentists, periodontists had the highest success rate, with an area under the receiver operating characteristic curve (AUC) value of 0.937 ± 0.083. However, no significant differences were observed in the success rates among the dentists based on their specialty, years of professional experience, or previous experience with oral cancer monitoring (p <0.05). Based on the test conducted on 32 images, the most successful models were ResNet-101 and Inception v3, with an AUC value of 0.958. Conclusion: The rapid advancements in DL are associated with numerous advantages in various fields, including healthcare, especially with regard to achieving accurate diagnoses. These benefits also extend to oral cancers. We found that data-driven AI systems can make a reliable and objective diagnosis of oral cancer.