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Deep Learning for Diagnosis of Head and Neck Cancers Through Radiographic Data: A Systematic Review and Meta-Analysis Publisher Pubmed



Rokhshad R1 ; Salehi SN2 ; Yavari A3 ; Shobeiri P4 ; Esmaeili M5 ; Manila N1, 6 ; Motamedian SR1, 7 ; Mohammadrahimi H1
Authors
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Authors Affiliations
  1. 1. Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
  2. 2. Executive Secretary of Research Committee, Board Director of Scientific Society, Dental Faculty, Azad University, Tehran, Iran
  3. 3. Student Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. School of Medicine, Tehran University of Medical Science, Tehran, Iran
  5. 5. Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
  6. 6. Department of Diagnostic Sciences, Louisiana State University Health Science Center School of Dentistry, LA, United States
  7. 7. Dentofacial Deformities Research Center, Research Institute of Dental, Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjou Blvd, Tehran, Iran

Source: Oral Radiology Published:2024


Abstract

Purpose: This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data. Methods: Through January 2023, a PubMed, Scopus, Embase, Google Scholar, IEEE, and arXiv search were carried out. The inclusion criteria were implementing head and neck medical images (computed tomography (CT), positron emission tomography (PET), MRI, Planar scans, and panoramic X-ray) of human subjects with segmentation, object detection, and classification deep learning models for head and neck cancers. The risk of bias was rated with the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. For the meta-analysis diagnostic odds ratio (DOR) was calculated. Deeks’ funnel plot was used to assess publication bias. MIDAS and Metandi packages were used to analyze diagnostic test accuracy in STATA. Results: From 1967 studies, 32 were found eligible after the search and screening procedures. According to the QUADAS-2 tool, 7 included studies had a low risk of bias for all domains. According to the results of all included studies, the accuracy varied from 82.6 to 100%. Additionally, specificity ranged from 66.6 to 90.1%, sensitivity from 74 to 99.68%. Fourteen studies that provided sufficient data were included for meta-analysis. The pooled sensitivity was 90% (95% CI 0.820.94), and the pooled specificity was 92% (CI 95% 0.87–0.96). The DORs were 103 (27–251). Publication bias was not detected based on the p-value of 0.75 in the meta-analysis. Conclusion: With a head and neck screening deep learning model, detectable screening processes can be enhanced with high specificity and sensitivity. © 2023, The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.
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