Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Abstract

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology

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Optimization of Support Vector Machines by Meta-Heuristic Approach for Differential Diagnosis and Classification of Oral Diseases

“…A wide variety of machine learning methods of data mining and artificial intelligence have been used for feature selection and classification in the diagnosis of diseases. Researchers have found that the use of modern techniques along with the main diagnosis and treatment templates can be far more practical and accurate [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], and [13].…”

Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis

“…Furthermore from our previous experience, machine learning algorithms have been applied to various fields in genomics (9), healthcare (10), computer vision (Malik et al 2021) etc. As the applications of these methods have assisted precision medicine scale, this would eventually bridge the gaps in oral squamous cell carcinoma (12). Ahmed et al have earlier investigated these methods from the AI dental imaging perspective.…”