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

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 canc...

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Bibliographic Details
Published in:Cancers 2021-09, Vol.13 (18), p.4600
Main Authors: García-Pola, María, Pons-Fuster, Eduardo, Suárez-Fernández, Carlota, Seoane-Romero, Juan, Romero-Méndez, Amparo, López-Jornet, Pia
Format: Article
Language:English
Subjects:
Artificial intelligence
Cancer screening
Cytology
Decision making
Diagnosis
Learning algorithms
Machine learning
Morbidity
Mortality
Neural networks
Systematic Review
Telemedicine
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Summary: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.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers13184600