Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

Abstract

The early diagnosis of cancer can facilitate subsequent clinical patient management. Artificial intelligence (AI) has been found to be promising for improving the diagnostic process. The aim of the present study is to increase the evidence on the application of AI to the early diagnosis of oral cancer through a scoping review. A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer based on AI applied to screening. Only accessible full-text articles were considered. Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models. These studies were characterized by their heterogeneous nature. Each publication involved a different algorithm with potential training data bias and few comparative data for AI interpretation. Artificial intelligence may play an important role in precisely predicting the development of oral cancer, though several methodological issues need to be addressed in parallel to the advances in AI techniques, in order to allow large-scale transfer of the latter to population-based detection protocols.

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

“…The quality appraisal for all the review papers considered in this study indicated that 42.9% showed high quality [ 7 , 21 , 22 ] while 57.1% showed medium quality [ 18 – 20 , 23 ]. Three of the reviews were conducted in the year 2020 [ 18 – 20 ] while four were conducted in the year 2021 [ 7 , 21 – 23 ].…”

“…Of the 7 included systematic reviews [7,[18][19][20][21][22][23], 4 were conducted in Asia (i.e., 2 each from China and India) [18,19,21,22] while 2 reviews was conducted in Europe (Finland) [7,23] and one review in the United States [20] (Table 2). The quality appraisal for all the review papers considered in this study indicated that 42.9% showed high quality [7,21,22] while 57.1% showed medium quality [18][19][20]23]. Three of the reviews were conducted in the year 2020 [18][19][20] while four were conducted in the year 2021 [7,[21][22][23].…”

“…Several data types such as pathologic and radiographic images have been used by deep learning in the quest to achieve precise diagnosis and prognosis [20,[22][23][24][25][26][27][28][29][30][31][32]. Other data types include gene expression data, spectra data, saliva metabolites, autofluorescence, cytology-image, computed tomography images, and clinicopathologic images that have been used in the deep learning analysis for improved diagnosis of oral cancer [7].…”

“…The trained deep learning model has the potential of producing a high clinical translatability in the proper management of oral cancer patients García-Pola et al[23] (Spain) Role of artificial intelligence in the early diagnosis of oral cancer. A scoping review 36 Artificial intelligence can help in the detection of oral premalignant disorder.…”

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

“…Considering these findings within the current study's context, the application of our method (which utilizes ANNs) may serve as a useful adjunct to dentists' care process by providing a second set of eyes to account for the possibility of human error. García-Pola et al (59) reported that AI provides excellent opportunities for the automation of tasks in medical and dental practice through the detection of complex patterns. Using ML to automate patient chart review and data analysis will decrease dentists' time spent on routine work (e.g., retrieving and analyzing patients' records) and ultimately create greater opportunities for them to engage in humanizing care (i.e., the face-to-face time between dentists and their patients) and discuss their findings with patients (60).…”