Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

Abstract

The accuracy of artificial intelligence (AI)-assisted discrimination of oral cancerous lesions from normal mucosa based on mucosal images was evaluated. Two authors independently reviewed the database until June 2022. Oral mucosal disorder, as recorded by photographic images, autofluorescence, and optical coherence tomography (OCT), was compared with the reference results by histology findings. True-positive, true-negative, false-positive, and false-negative data were extracted. Seven studies were included for discriminating oral cancerous lesions from normal mucosa. The diagnostic odds ratio (DOR) of AI-assisted screening was 121.66 (95% confidence interval [CI], 29.60; 500.05). Twelve studies were included for discriminating all oral precancerous lesions from normal mucosa. The DOR of screening was 63.02 (95% CI, 40.32; 98.49). Subgroup analysis showed that OCT was more diagnostically accurate (324.33 vs. 66.81 and 27.63) and more negatively predictive (0.94 vs. 0.93 and 0.84) than photographic images and autofluorescence on the screening for all oral precancerous lesions from normal mucosa. Automated detection of oral cancerous lesions by AI would be a rapid, non-invasive diagnostic tool that could provide immediate results on the diagnostic work-up of oral cancer. This method has the potential to be used as a clinical tool for the early diagnosis of pathological lesions.

A Novel Method for the Detection of Oral Cancers: Deep Learning

“…Integration of AI-supported apps into mobile phones can effectively facilitate oral cancer screening in rural areas with limited access to healthcare services [24]. A previous study evaluated algorithms for the diagnosis and classi cation of oral cancers using intraoral photographs, and found that algorithms outperformed general practitioners and exhibited a performance similar to that of specialist physicians [25]. In our study, periodontists were the most successful group of dental professionals in diagnosing oral lesions.…”

Artificial Intelligence (AI) Driven Digital Dentistry

“…Radiographic, microscopic, and ultrasonographic pictures have mostly been used in AI research for tumor and cancer identification. In addition, aberrant spots on radiographs, such as salivary and parotid glands and nerves in the oral cavity, can be found by AI [7][8] . Notably, AI also contributes to the management of cleft lip and palate in terms of risk assessment, diagnosis, pre-surgical orthopedics, speech evaluation, and surgery 9 .…”

Construction of the Interaction Network of Hub Genes in the Progression of Barrett’s Esophagus to Esophageal Adenocarcinoma

“… 29 Precancerous lesions are an intermediate stage in the transformation of normal cells to cancer cells, which have similar and different genetic characteristics from tumors. 30 , 31 Moreover, they differ in their biological behavior and clinical manifestations, such as cell proliferation ability, motility, etc., which may be associated with progressive abnormal expression of oncogenes and tumor suppressor genes. In the present study, we identified progressive upregulated oncogenes and downregulated oncogenes in the disease process, which may be key factors influencing the process of NE-BE-EAC.…”