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Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview
Hanya Mahmood1, Muhammad Shaban2, Nasir Rajpoot2
1Academic Unit of Oral & Maxillofacial Surgery, School of Clinical Dentistry, University of Sheffield, Sheffield, UK. h.mahmood@sheffield.ac.uk.
British Journal of Cancer
|April 20, 2021
Summary
Artificial Intelligence/Machine Learning (AI/ML) shows promise in head and neck cancer (HNC) diagnostics through automated image analysis. Further large-scale studies are needed for clinical integration of these accurate AI/ML methods.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Review of recent literature on Artificial Intelligence/Machine Learning (AI/ML) applications in head and neck cancer (HNC) diagnostics.
- Focus on automated image analysis for HNC evaluation.
Purpose of the Study:
- To systematically review AI/ML methods used in diagnostic evaluation of HNC.
- To identify imaging modalities and AI/ML techniques applied in HNC research.
Main Methods:
- Comprehensive electronic database searches (MEDLINE, EMBASE, Google Scholar) from 2009-2020.
- Inclusion of studies utilizing AI/ML for HNC diagnostic evaluation, irrespective of method or imaging modality.
- Identification and categorization of HNC sites, imaging modalities, and AI/ML approaches.
Main Results:
- 32 articles were identified, focusing on various HNC sites like the oral cavity (16 studies).
- Diverse imaging modalities were used, including histological, radiological, and hyperspectral imaging.
- Traditional ML methods dominated (69%), followed by deep learning (25%).
Conclusions:
- Growing body of research highlights AI/ML's potential in aiding HNC detection via automated image analysis.
- AI/ML methods demonstrate high accuracy, potentially surpassing human judgment in data predictions.
- Large-scale, multi-centric prospective studies are crucial for clinical implementation.
