A study on Medical Imaging Modalities and Artificial Intelligence methods for Identification of Oral Cancer
Keywords:
Cancer, oral cancer, squamous cell carcinoma, risk factor, medical imagingAbstract
All disorders that result in abnormal and uncontrolled cell division and development are collectively referred to as cancers. One of the more prevalent cancer types identified throughout the global community is oral cancer. A variety of modifiable risk factors, including sugar consumption, tobacco use, alcohol use, poor hygiene, and their underlying societal and economic determinants, contribute to the development of oral cancer. These risk factors are also similar to many non-communicable diseases (NCDs). Due to infection with the human papilloma virus, 16, (HPV 16), it’s also a novel factor for causing oral cancer without any tobacco association. Oral cancer, also known as oral squamous cell carcinoma (OSCC), is an ulceroproliferative oral mucosa lesion that can impact any mouth part, from the lips to the oropharynx. For the management of OSCC, variants in the composition of patients, clinical paradigms, and technological advances offer both opportunities and challenges. Imaging remains an increasingly significant component in the staging, planning, and monitoring of patients with OSCC. Molecular and cellular changes in cells can now be detected non-invasively using imaging methods. The Artificial Intelligence approaches are also being utilized to enhance their incorporation into routine therapeutic operations. This study primarily focused on AI aspects of oral cancer identification. It provides a comprehensive overview of the existing imaging modalities, prominent AI models for identification, their performance, and their limitations. In the current review, we summarize the current progress of machine learning, deep learning, and transfer learning in OSCC detection, with a particular focus on methods of classification.
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