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Review
. 2023 Apr 5;13(7):1353.
doi: 10.3390/diagnostics13071353.

A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions

Shriniket Dixit  1 , Anant Kumar  2 , Kathiravan Srinivasan  1
Affiliations
Review

A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions

Shriniket Dixit et al. Diagnostics (Basel). .

Abstract

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people's lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI's drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.

Keywords: artificial intelligence; computer-aided diagnostics; deep learning; machine learning; man–machine systems; oral cancer diagnosis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Inquiries made using key terms in databases.
Figure 2
Figure 2
Articles were identified using the PRISMA method.
Figure 3
Figure 3
Number of papers per year, used in the review.
Figure 4
Figure 4
ML models vs. frequency of papers used in this work.
Figure 5
Figure 5
DL models vs. frequency of papers used in this work.
Figure 6
Figure 6
Structure of this article.
Figure 7
Figure 7
Classification of OC based on the origin location of lesion.
Figure 8
Figure 8
Technologies related to diagnosis of OC.
Figure 9
Figure 9
ML Models for Oral Cancer Diagnosis used in this review.
Figure 10
Figure 10
DL Models for Oral Cancer Diagnosis used in this review.
Figure 11
Figure 11
Open Challenges for Oral Cancer Diagnosis.
Figure 12
Figure 12
Future Research Directions for Oral Cancer Diagnosis.
See this image and copyright information in PMC

References

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