Artificial Intelligence Helps Diagnose Oral Potentially Malignant Disorders: A Systematic Review and Meta-Analysis.

IF 2 Q2 DENTISTRY, ORAL SURGERY & MEDICINE

JDR Clinical & Translational Research Pub Date : 2026-10-01 Epub Date: 2026-01-21 DOI:10.1177/23800844251403962

D Horvath, A Fekete, P Martinekova, S Kiss-Dala, E Abram, K Marton, A Zsembery, P Hegyi, A Brody

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引用次数: 0

Abstract

Introduction: Oral potentially malignant disorders (OPMDs) can lead to oral cancer, which is one of the most common cancers worldwide. Prevention is crucial in the avoidance of malignant transformations of OPMDs. Artificial intelligence (AI) provides a new and noninvasive tool for analyzing medical data, such as patient data, radiologic images, and clinical photographs. These AI-based tools can help in the decision-making process. However, histological examination is still the gold standard for diagnosing OPMDs.

Objectives: This systematic review and meta-analysis aimed to investigate the diagnostic accuracy of artificial intelligence on intraoral photographs of patients with OPMDs.

Methods: A systematic search was conducted on 5 major databases (MEDLINE, Embase, Cochrane Library, Scopus, and Web of Science) on November 10, 2023. Included studies compared AI methods to histology examination as the reference. A quantitative analysis was carried out to assess sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), diagnostic odds ratio (DOR), positive likelihood ratio (LR⁺), and negative likelihood ratio (LR^-) calculated with 95% confidence intervals (CIs).

Results: Six eligible articles were included, with 898 images out of 4,046 tested using AI-based architectures. Five studies investigated at least 2 AI models. The overall sensitivity, specificity, DOR, LR⁺, and LR^- were 0.94 (95% CI, 0.88 to 0.95), 0.95 (95% CI, 0.85 to 0.98), 212.39 (95% CI, 56.39 to 800.00), 16.89 (95% CI, 5.72 to 48.68), and 0.08 (95% CI, 0.05 to 0.13) for the best-performing AI-based architectures in terms of sensitivity, respectively.

Conclusion: AI-based diagnostic tools have high negative predictive value that could help identify OPMD lesions using intraoral photographs.Knowledge Transfer Statement:This systematic review on AI-based methods to diagnose oral potentially malignant disorders showed that although their high negative predictive value could reduce unnecessary specialist consultations, clinical judgment remains paramount. Further prospective studies are needed to evaluate the integration of AI diagnostics into routine care and screening and policies to enhance efficiency and support early detection and prevention of oral cancer.

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人工智能有助于诊断口腔潜在恶性疾病：一项系统综述和荟萃分析。

口腔潜在恶性疾病（OPMDs）可导致口腔癌，口腔癌是世界上最常见的癌症之一。预防是避免opmd恶性转化的关键。人工智能（AI）为分析医疗数据（如患者数据、放射图像和临床照片）提供了一种新的无创工具。这些基于人工智能的工具可以帮助决策过程。然而，组织学检查仍然是诊断opmd的金标准。目的：本系统综述和荟萃分析旨在探讨人工智能对opmd患者口内照片的诊断准确性。方法：于2023年11月10日对MEDLINE、Embase、Cochrane Library、Scopus、Web of Science 5大数据库进行系统检索。纳入的研究将人工智能方法与组织学检查相比较作为参考。进行定量分析，评估敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）、诊断优势比（DOR）、阳性似然比（LR+）和阴性似然比（LR-），并以95%置信区间（ci）计算。结果：包括6篇符合条件的文章，使用基于人工智能的架构测试了4,046张图像中的898张。五项研究调查了至少两种人工智能模型。就敏感性而言，表现最佳的基于人工智能的架构的总体敏感性、特异性、DOR、LR+和LR-分别为0.94 （95% CI， 0.88至0.95）、0.95 （95% CI， 0.85至0.98）、212.39 （95% CI， 56.39至800.00）、16.89 （95% CI， 5.72至48.68）和0.08 （95% CI， 0.05至0.13）。结论：基于人工智能的诊断工具具有较高的阴性预测值，可以通过口腔内照片识别OPMD病变。知识转移声明：这项基于人工智能的口腔潜在恶性疾病诊断方法的系统综述表明，尽管它们的高阴性预测值可以减少不必要的专家咨询，但临床判断仍然是最重要的。需要进一步的前瞻性研究来评估将人工智能诊断纳入常规护理和筛查以及政策，以提高效率并支持早期发现和预防口腔癌。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

JDR Clinical & Translational Research DENTISTRY, ORAL SURGERY & MEDICINE-

CiteScore

6.20

自引率

6.70%

发文量

期刊介绍： JDR Clinical & Translational Research seeks to publish the highest quality research articles on clinical and translational research including all of the dental specialties and implantology. Examples include behavioral sciences, cariology, oral & pharyngeal cancer, disease diagnostics, evidence based health care delivery, human genetics, health services research, periodontal diseases, oral medicine, radiology, and pathology. The JDR Clinical & Translational Research expands on its research content by including high-impact health care and global oral health policy statements and systematic reviews of clinical concepts affecting clinical practice. Unique to the JDR Clinical & Translational Research are advances in clinical and translational medicine articles created to focus on research with an immediate potential to affect clinical therapy outcomes.