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. 2024 Nov 6:5:1494867.
doi: 10.3389/froh.2024.1494867. eCollection 2024.

Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis

Rakesh Kumar Sahoo  1   2 , Krushna Chandra Sahoo  3 , Girish Chandra Dash  4 , Gunjan Kumar  5 , Santos Kumar Baliarsingh  6 , Bhuputra Panda  1 , Sanghamitra Pati  2
Affiliations

Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis

Rakesh Kumar Sahoo et al. Front Oral Health. .

Abstract

Objective: Oral cancer is a widespread global health problem characterised by high mortality rates, wherein early detection is critical for better survival outcomes and quality of life. While visual examination is the primary method for detecting oral cancer, it may not be practical in remote areas. AI algorithms have shown some promise in detecting cancer from medical images, but their effectiveness in oral cancer detection remains Naïve. This systematic review aims to provide an extensive assessment of the existing evidence about the diagnostic accuracy of AI-driven approaches for detecting oral potentially malignant disorders (OPMDs) and oral cancer using medical diagnostic imaging.

Methods: Adhering to PRISMA guidelines, the review scrutinised literature from PubMed, Scopus, and IEEE databases, with a specific focus on evaluating the performance of AI architectures across diverse imaging modalities for the detection of these conditions.

Results: The performance of AI models, measured by sensitivity and specificity, was assessed using a hierarchical summary receiver operating characteristic (SROC) curve, with heterogeneity quantified through I2 statistic. To account for inter-study variability, a random effects model was utilized. We screened 296 articles, included 55 studies for qualitative synthesis, and selected 18 studies for meta-analysis. Studies evaluating the diagnostic efficacy of AI-based methods reveal a high sensitivity of 0.87 and specificity of 0.81. The diagnostic odds ratio (DOR) of 131.63 indicates a high likelihood of accurate diagnosis of oral cancer and OPMDs. The SROC curve (AUC) of 0.9758 indicates the exceptional diagnostic performance of such models. The research showed that deep learning (DL) architectures, especially CNNs (convolutional neural networks), were the best at finding OPMDs and oral cancer. Histopathological images exhibited the greatest sensitivity and specificity in these detections.

Conclusion: These findings suggest that AI algorithms have the potential to function as reliable tools for the early diagnosis of OPMDs and oral cancer, offering significant advantages, particularly in resource-constrained settings.

Systematic review registration: https://www.crd.york.ac.uk/, PROSPERO (CRD42023476706).

Keywords: AI algorithms; OPMDs; deep learning; diagnostic performance; early detection; medical diagnostic imaging; oral cancer.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
PRISMA flow diagram.
Figure 2
Figure 2
Distribution of the studies across the globe.
Figure 3
Figure 3
Overall SROC plot for various image-based diagnostic performance of artificial intelligence algorithms for detecting OPMDs & oral cancer.
Figure 4
Figure 4
Crosshair plot for various image-based diagnostic performance of artificial intelligence algorithms for detecting OPMDs & oral cancer.
See this image and copyright information in PMC

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References

    1. Chi AC, Day TA, Neville BW. Oral cavity and oropharyngeal squamous cell carcinoma–an update. CA Cancer J Clin. (2015) 65(5):401–21. 10.3322/caac.21293 - DOI - PubMed
    1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. (2021) 71(3):209–49. 10.3322/caac.21660 - DOI - PubMed
    1. Warnakulasuriya S. Global epidemiology of oral and oropharyngeal cancer. Oral Oncol. (2009) 45(4–5):309–16. 10.1016/j.oraloncology.2008.06.002 - DOI - PubMed
    1. Song B, Sunny S, Li S, Gurushanth K, Mendonca P, Mukhia N, et al. Mobile-based oral cancer classification for point-of-care screening. J Biomed Opt. (2021) 26(6):065003. 10.1117/1.JBO.26.6.065003 - DOI - PMC - PubMed
    1. Jemal A, Ward EM, Johnson CJ, Cronin KA, Ma J, Ryerson B, et al. Annual report to the nation on the Status of cancer, 1975–2014, featuring survival. J Natl Cancer Inst. (2017) 109(9). 10.1093/jnci/djx030 - DOI - PMC - PubMed

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