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Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions : a systematic review
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Mahmood, H., Shaban, Muhammad, Indave, B.I., Santos-Silva, A.R., Rajpoot, Nasir M. (Nasir Mahmood) and Khurram, S.A. (2020) Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions : a systematic review. Oral Oncology, 110 . 104885. doi:10.1016/j.oraloncology.2020.104885
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Official URL: http://dx.doi.org/10.1016/j.oraloncology.2020.1048...
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.
| Item Type: | Journal Article | ||||||||
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| Subjects: | L Education > LB Theory and practice of education Q Science > Q Science (General) R Medicine > R Medicine (General) |
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| Divisions: | Faculty of Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Artificial intelligence, Machine learning, Head -- Cancer, Neck -- Cancer, Cancer, Dysplasia, Learning, Systematic reviews (Medical research) | ||||||||
| Journal or Publication Title: | Oral Oncology | ||||||||
| Publisher: | Elsevier Science BV | ||||||||
| ISSN: | 1368-8375 | ||||||||
| Official Date: | November 2020 | ||||||||
| Dates: |
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| Volume: | 110 | ||||||||
| Article Number: | 104885 | ||||||||
| DOI: | 10.1016/j.oraloncology.2020.104885 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access |
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