Mahmood, H. orcid.org/0000-0001-7159-0368, Shaban, M., Indave, B.I. et al. (3 more authors) (2020) Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review. Oral Oncology, 110. 104885. ISSN 1368-8375
Abstract
This systematic review analyses and describes the application and diagnostic accuracy of Artificial Intelligence (AI) methods used for detection and grading of potentially malignant (pre-cancerous) and cancerous head and neck lesions using whole slide images (WSI) of human tissue slides. Electronic databases MEDLINE via OVID, Scopus and Web of Science were searched between October 2009 – April 2020. Tailored search-strings were developed using database-specific terms. Studies were selected using a strict inclusion criterion following PRISMA Guidelines. Risk of bias assessment was conducted using a tailored QUADAS-2 tool. Out of 315 records, 11 fulfilled the inclusion criteria. AI-based methods were employed for analysis of specific histological features for oral epithelial dysplasia (n = 1), oral submucous fibrosis (n = 5), oral squamous cell carcinoma (n = 4) and oropharyngeal squamous cell carcinoma (n = 1). A combination of heuristics, supervised and unsupervised learning methods were employed, including more than 10 different classification and segmentation techniques. Most studies used uni-centric datasets (range 40–270 images) comprising small sub-images within WSI with accuracy between 79 and 100%. This review provides early evidence to support the potential application of supervised machine learning methods as a diagnostic aid for some oral potentially malignant and malignant lesions; however, there is a paucity of evidence using AI for diagnosis of other head and neck pathologies. Overall, the quality of evidence is low, with most studies showing a high risk of bias which is likely to have overestimated accuracy rates. This review highlights the need for development of state-of-the-art deep learning techniques in future head and neck research.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Ltd. This is an author produced version of a paper subsequently published in Oral Oncology. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Artificial intelligence; Machine learning; Head and neck cancer; Oral cancer; Pre-cancer; Oral potentially malignant disorders; dysplasia; squamous cell carcinoma; deep learning; systematic review |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Clinical Dentistry (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Jul 2020 08:58 |
Last Modified: | 13 Jul 2021 00:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.oraloncology.2020.104885 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163256 |
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Filename: Final manuscript - clean version with revisions.pdf
Licence: CC-BY-NC-ND 4.0