Artificial intelligence for oral cancer diagnosis: What are the possibilities?. (November 2022)

• Record Type: Journal Article
• Authors: Tobias, Mattheus A.S.; Nogueira, Bruna P.; Santana, Marcos C.S.; Pires, Rafael G.; Papa, João P.; Santos, Paulo S.S.
• Is Part Of: Oral oncology. Volume 134(2022)
• Publication Date: 2022-11
• Subjects: Mouth neoplasms -- Machine learning -- Diagnosis, oral -- Artificial intelligence -- Carcinoma, squamous cell; AI Artificial Intelligence; Mouth -- Cancer -- Periodicals; Mouth -- Tumors -- Periodicals; Mouth Diseases -- Periodicals; Mouth Neoplasms -- Periodicals; Bouche -- Cancer -- Périodiques; Bouche -- Tumeurs -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9943105
• DOI: 10.1016/j.oraloncology.2022.106117
• Languages: English
• ISSNs: 1368-8375

Abstract

Highlights: Oral cancer's primary strategy is based on prevention. Most patients are diagnosed at an advanced cancer staging (III and IV). AI could assist in oral cancer diagnosis, but few studies have been conducted. A convoluted neural network has been training based on photographic images. The accuracy results are acceptable and compatible with recent literature. Abstract: Oral cancer could be prevented. The primary strategy is based on prevention. Most patients with oral cancer present to the hospital network with advanced staging and a low chance of cure. This condition may be related to physicians' difficulty of making an early diagnosis. With the advancement of information technology, artificial intelligence (AI) holds great promise in terms of assisting in diagnosis. Few machine learning algorithms have been developed for this purpose to date. In this paper, we will discuss the possibilities for diagnosing oral cancer using AI as a tool, as well as the implications for the population. A set of photographic images of oral lesions has been segmented, indicating not only the area of the lesion but also the class of lesion associated with it. Different neural network architectures were trained with the goal of fine segmentation (pixel by pixel), classification of image crops, and classification of whole images based on the presence or absence of a lesion. The accuracy results are acceptable, opening up possibilities not only for identifying lesions but also for classifying the pathology associated with them.