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Systematic Review

Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence

by
Waseem Jerjes
1,2,*,
Harvey Stevenson
2,
Daniele Ramsay
2 and
Zaid Hamdoon
3
1
Research and Development Unit, Hammersmith and Fulham Primary Care Network, London W6 7HY, UK
2
Faculty of Medicine, Imperial College London, London W12 0BZ, UK
3
Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(19), 5822; https://doi.org/10.3390/jcm13195822 (registering DOI)
Submission received: 27 December 2023 / Revised: 25 September 2024 / Accepted: 26 September 2024 / Published: 29 September 2024
(This article belongs to the Section Dentistry, Oral Surgery and Oral Medicine)

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.
Keywords: OCT; oral cancer; diagnostic accuracy; artificial intelligence; imaging modalities OCT; oral cancer; diagnostic accuracy; artificial intelligence; imaging modalities

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MDPI and ACS Style

Jerjes, W.; Stevenson, H.; Ramsay, D.; Hamdoon, Z. Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence. J. Clin. Med. 2024, 13, 5822. https://doi.org/10.3390/jcm13195822

AMA Style

Jerjes W, Stevenson H, Ramsay D, Hamdoon Z. Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence. Journal of Clinical Medicine. 2024; 13(19):5822. https://doi.org/10.3390/jcm13195822

Chicago/Turabian Style

Jerjes, Waseem, Harvey Stevenson, Daniele Ramsay, and Zaid Hamdoon. 2024. "Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence" Journal of Clinical Medicine 13, no. 19: 5822. https://doi.org/10.3390/jcm13195822

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