Classification Of Oral Precancer And Cancer Using Image-based Artificial Intelligence Algorithms: Protocol For A Scoping Review - Info and Reading Options

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Cancers of the oral cavity are one of the most prevalent cancers worldwide. With highest incidence in developing countries in Asia, they have shown huge variation in geographical distribution. In Taiwan, oral cancer is the 6th most common cancer overall and the 4th most common cancer among men. Oral cavity cancer often results in impaired functions and esthetics like difficulty in breathing, swallowing, mastication, speech along with disfigurement of the face. Advanced stage oral cancer is associated with high mortality rates and poses a serious threat to public health. Around 85% of these malignant lesions are diagnosed as oral squamous cell carcinoma (OSCC) and this diagnosis is confirmed through tissue biopsy. Although currently considered as gold standard, histopathological classification and tissue biopsies (surgical biopsy, punch biopsy, lymph node biopsy, brush biopsy, and needle aspiration biopsy) are invasive, time consuming and cannot be repeated frequently. Further, prediction of dysplastic oral lesions and OSCC only through visual inspection is challenging and demands significant training and expertise. It is therefore crucial to explore alternative diagnostic/screening tools like Artificial Intelligence (AI) and Machine Learning (ML) tools for image classification. Currently, deep learning (DL) is a dominant machine learning (ML) technique establishing new capability standards for image classification in a variety of domains including medicine. DL has shown promising results in several image-based medical screening and diagnostic applications. It is important to learn the progress and development of work in the literature on using ML especially DL techniques for automatic oral cancer image analysis/classification. To help understand what has been done, identify the challenges and opportunities, address its research gap, and improve its status of the art, we propose to conduct a scoping review on this topic. Study Objective: This scoping review aims at mapping the studies and synthesizing the evidence on the artificial intelligence and/or machine learning techniques available for the classification of intraoral images to identify oral cancers or precancers. Furthermore, the review will also explore and compare the performance of the models/techniques when used for classification of oral cancer and/or precancer and how they aid in diagnosis made visually. The research gap will be identified and will be used to provide direction for future studies in this area.

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