Artificial intelligence helps diagnose oral potentially malignant disorders
Description
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 non-invasive tool for analyzing medical data, such as patient data, radiological 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. Our research aimed to investigate the diagnostic accuracy of artificial intelligence on intraoral photographs of patients with OPMDs. On November 10, 2023, a systematic search was conducted on five major databases: MEDLINE, Embase, Cochrane Library, Scopus, and Web of Science. From the available data, we performed a quantitative analysis for sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), diagnostic odds ratio (DOR), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) and calculated their 95% confidence intervals (CI). Our search resulted in 6 eligible articles, in which 898 images out of a total of 4,046 were tested using AI-based architectures. In five of the six studies, at least two AI models were investigated. Among the eligible articles, two independent authors (DH, ÁF) collected data on decision making in the OPMD classification of the artificial intelligence-supported tool and filled out a customized data extraction sheet designed for this review. Our data analysis units covered sensitivity, specificity, and confusion matrix. In addition, the following information was collected: first author, publication year, study period, teaching-, validation-, testing sample size, teaching settings (epochs, batch size, and learning rate), training methods, and augmentation types. Information (number of layers and parameters) on the AI-based models used was collected from the internet if not provided in the article. Some data about the tested AI-based architectures were collected from the internet if not included in the article. The sources for this information were https://github.com and https://pytorch.org.