Document Type : Systematic Review and Meta-analysis
Authors
1
BDS, Bharati Vidyapeeth (Deemed to be University), Dental College and Hospital, Pune, Maharashtra, India.
2
Assistant Professor, Department of Public Health Dentistry Bharati Vidyapeeth (Deemed to be University), Dental College and Hospital, Pune, Maharashtra, India.
3
Professor, Department of Oral Medicine and Radiology, Institute of Dental sciences, Siksha O Anusandhan University, Bhubaneswar, Odisha, India.
4
Assistant Professor, Department of Oral Medicine and Radiology Bharati Vidyapeeth (Deemed to be University), Dental College and Hospital, Sangli, Maharashtra, India.
5
Professor, Department of Prosthodontics, Institute of Dental Sciences, Siksha O Anusandhan University, Bhubaneshwar, Odisha, India.
6
Assistant Professor, Bharati Vidyapeeth (Deemed to be University), Dental College and Hospital, Pune, Maharashtra, India.
Abstract
Objective: To summarize and compare the existing evidence on diagnostic accuracy of artificial intelligence (AI) models in detecting early oral squamous cell carcinoma (OSCC). Method: Review was performed in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis – Diagnostic Test Accuracy (PRISMA- DTA) checklist and the review protocol is registered under PROSPERO(CRD42023456355). PubMed, Google Scholar, EBSCOhost were searched from January 2000 to November 2023 to identify the diagnostic potential of AI based tools and models. True-positive, false-positive, true-negative, false-negative, sensitivity, specificity values were extracted or calculated if not present for each study. Quality of selected studies was evaluated based on QUADAS (Quality assessment of diagnostic accuracy studies)- 2 tool. Meta-analysis was performed in Meta-Disc 1.4 software and Review Manager 5.3 RevMan using a bivariate model parameter for the sensitivity and specificity and summary points, summary receiver operating curve (SROC), diagnostic odds ratio (DOR) confidence region, and area under curve (AUC) were calculated. Results: Fourteen studies were included for qualitative synthesis and for meta-analysis. Included studies had presence of low to moderate risk of bias. Pooled sensitivity and specificity of 0.43 (CI 0.18- 0.71) and 0.50 (CI 0.20- 0.80) was observed with a pooled positive likelihood ratio of (PLR) 0.86 (0.43 – 1.71) and negative likelihood ratio (NLR) of 1.04 (0.42 – 1.68) was observed with DOR of 0.78 (0.12 – 5.18) and overall accuracy (AUC) being 0.45 respectively. Conclusion: AI based tools has poor to moderate overall diagnostic accuracy. However, to validate our study findings further more standardized diagnostic accuracy studies should be conducted with proper reporting through QUADAS-2 tool. Thus, we can conclude AI based based tool for secondary level of prevention for early OSCC under early diagnosis and prompt treatment.
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