Department of Orthodontics and Dentofacial Orthopedics, School of Dentistry, Zanjan University of Medical Sciences, Zanjan, Iran.
Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
J Oral Pathol Med. 2024 Oct;53(9):551-566. doi: 10.1111/jop.13578. Epub 2024 Sep 10.
Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error.
This systematic review and meta-analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI-based medical image analysis studies.
Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS-2 tool was utilized to assess quality. The meta-analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets.
Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS-2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta-analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity.
Application of AI-based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI-based models cannot replace a well-trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout.
人工智能（AI）工具在提高口腔鳞状细胞癌（OSCC）检测准确性方面显示出在组织病理学图像分析方面的应用前景，旨在减少人为错误。
本系统评价和荟萃分析评估了基于深度学习（DL）的模型在组织病理学图像上检测 OSCC 的性能，评估了基于人工智能的医学图像分析研究的常见诊断性能评估指标。
分析了使用 DL 模型对 OSCC 组织病理学图像进行分析并与参考标准进行比较的诊断准确性研究。无时间限制地在 6 个数据库（PubMed、Google Scholar、Scopus、Embase、ArXiv 和 IEEE）中筛选了出版物。使用 QUADAS-2 工具评估质量。荟萃分析仅包括在其测试集中报告真阳性（TP）、真阴性（TN）、假阳性（FP）和假阴性（FN）的研究。
在筛选出的 1267 项研究中，有 17 项研究最终符合纳入标准。使用了 DL 方法，如图像分类（n=11）和分割（n=3），有些研究使用了联合方法（n=3）。在 QUADAS-2 评估中，只有 3 项研究在所有适用性领域均具有低偏倚风险。对于分割研究，报道的准确性为 0.97，灵敏度为 0.97，特异性为 0.98，Dice 为 0.92。对于分类研究，报道的准确性为 0.99，灵敏度为 0.99，特异性为 1.0，Dice 为 0.95，F1 分数为 0.98，AUC 为 0.99。荟萃分析显示，敏感性的合并估计值为 0.98，特异性为 0.93。
基于 AI 的分类和分割方法在图像分析中的应用代表了数字病理学的根本转变。DL 方法在组织病理学上检测 OSCC 的准确性显著提高，在某些研究中与人类专家的准确性相当。虽然基于 AI 的模型不能替代训练有素的病理学家，但它们可以通过提高诊断的客观性和可重复性，同时减少病理学家因疲劳而导致的变异性和人为错误，从而协助诊断。
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