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

Cancers (Basel)

Biomedical Research Institute (IMIB-Arrixaca), Faculty of Medicine and Odontology, Hospital Morales Meseguer, Clinica Odontologica Marqués del los Vélez s/n, 30008 Murcia, Spain.

Published: September 2021

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8467703PMC
http://dx.doi.org/10.3390/cancers13184600DOI Listing

Publication Analysis

Top Keywords

oral cancer
20
artificial intelligence
12
early diagnosis
12
diagnosis oral
12
cancer scoping
8
scoping review
8
cancer based
8
cancer
6
early
5
oral
5

Similar Publications

Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.

View Article and Find Full Text PDF

Background: Infantile fibrosarcoma (IFS) is a rare pediatric tumor of intermediate malignancy with high local aggressiveness that typically presents in young infants. Its occurrence in the head and neck region is rare. Complete non-mutilating surgical resection is often not possible, requiring multimodal treatment.

View Article and Find Full Text PDF

Background: There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC).

Objectives: We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined.

View Article and Find Full Text PDF

Non-smoking and Non-drinking Oral Cancer Patients Are at Higher Risk of Second Primary Tumours.

Oral Dis

December 2024

Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Objectives: This study aimed to explore differences in demographics, tumour characteristics and outcomes in oral squamous cell carcinoma (OSCC) patients with a history of non-smoking, non-drinking (NSND) versus smoking and/or drinking (SD).

Materials And Methods: Newly diagnosed OSCC patients undergoing curative surgical treatment were prospectively included in OncoLifeS, a data biobank. Cox regression analysis was performed yielding hazard ratios (HRs) and 95% confidence intervals (95%CIs).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!