Enhancing oral cancer detection: a systematic review of the diagnostic accuracy and future integration of optical coherence tomography with artificial intelligence
Author(s)
Jerjes, Waseem
Stevenson, Harvey
Ramsay, Daniele
Hamdoon, Zaid
Type
Journal Article
Abstract
Introduction: Optical Coherence Tomography (OCT) has emerged as an important imaging modality in non-invasive diagnosis for oral cancer and can provide real-time visualisation of tissue morphology with the required high resolution. This systematic review aims to assess the diagnostic accuracy of OCT in the detection of oral cancers, and to explore the potential integration of OCT with artificial intelligence (AI) and other imaging techniques to enhance diagnostic precision and clinical outcomes in oral healthcare. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, Google Scholar, Cochrane Central Register, and Web of Science from inception until August 2024. Studies were included if they employed OCT for oral cancer detection, reported diagnostic outcomes, such as sensitivity and specificity, and were conducted on human subjects. Data extraction and quality assessment were performed independently by two reviewers. The synthesis highlights advancements in OCT technology, including AI-enhanced interpretations. Results: A total of 9 studies met the inclusion criteria, encompassing a total of 860 events (cancer detections). The studies spanned from 2008 to 2022 and utilised various OCT techniques, including clinician-based, algorithm-based, and AI-driven interpretations. The findings indicate OCT’s high diagnostic accuracy, with sensitivity ranging from 75% to 100% and specificity from 71% to 100%. AI-augmented OCT interpretations demonstrated the highest accuracy, emphasising OCT’s potential in early cancer detection and precision in guiding surgical interventions. Conclusions: OCT could play a very prominent role as a new diagnostic tool for oral cancer, with very high sensitivity and specificity. Future research pointed towards integrating OCT with other imaging methods and AI systems in providing better accuracy of diagnoses, plus more clinical usability. Further development and validation with large-scale multicentre trials is imperative for the realisation of this potential in changing the way we practice oral healthcare.
Date Issued
2024-10-01
Date Acceptance
2024-09-26
Citation
Journal of Clinical Medicine, 2024, 13 (19)
ISSN
2077-0383
Publisher
MDPI AG
Journal / Book Title
Journal of Clinical Medicine
Volume
13
Issue
19
Copyright Statement
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39407882
PII: jcm13195822
Subjects
artificial intelligence
diagnostic accuracy
General & Internal Medicine
HEAD
imaging modalities
IN-VIVO
Life Sciences & Biomedicine
Medicine, General & Internal
OCT
oral cancer
Science & Technology
TISSUE
Publication Status
Published
Coverage Spatial
Switzerland
Article Number
5822
Date Publish Online
2024-09-29