Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.

Oral Oncology
H MahmoodS A Khurram

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 e...Continue Reading

Citations

Mar 31, 2021·Current Opinion in Oncology·Marion ClasseEric Deutsch
May 1, 2021·American Society of Clinical Oncology Educational Book·Lisanne V van Dijk, Clifton D Fuller
Apr 21, 2021·British Journal of Cancer·Hanya MahmoodSyed A Khurram
May 15, 2021·Pain Research & Management : the Journal of the Canadian Pain Society = Journal De La Société Canadienne Pour Le Traitement De La Douleur·Taseef Hasan FarookMohammad Khursheed Alam
Jun 14, 2021·Journal of Endodontics·Anita AminoshariaeVenkateshbabu Nagendrababu

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