Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: a systematic review and meta-analysis

Abstract

Background: The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially malignant disorders (OPMD) and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes.

Materials and methods: A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools.

Results: The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95% CI= [39.484-118.623], I2 =86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95% CI= [0.866-0.925]; I2 =81%), specificity of 89.2% (95% CI= [0.851-0.922], I2 =79%), and a high negative predictive value of 89.5% (95% CI= [0.851-0.927], I2 =96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95% CI [0.873-0.929], I2 =4%), 87.0% (95% CI [0.813-0.912], I2 =49%) and 90.1% (95% CI [0.860-0.931], I2 =57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy.

Conclusions: AI-based detection using clinical photography shows a high DOR and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment.

Figure 1

A flow diagram of the literature search and study selection process.

Figure 2

Risk of bias assessment.

Figure 3

Forest plots of (A) Diagnostic odds ratio, (B) Sensitivity, (C) Specificity, (D) negative predictive values for screening OPMD and oral mucosal cancerous lesions cancerous lesions with influential outliers excluded.

Figure 4

(A) Area under the summary receiver operating characteristic (SROC) for screening OPMD and oral mucosal cancerous lesions. (B) SROC for screening OPMD and oral mucosal cancerous lesions with influential outliers excluded.

Figure 5

GOSH plot of sensitivity and specificity data with outcome of k-means clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian unsupervised algorithms. Potentially influential studies are determined as those with a leverage 3 times above the mean in any generated cluster.

Figure 5

GOSH plot of sensitivity and specificity data with outcome of k-means clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian unsupervised algorithms. Potentially influential studies are determined as those with a leverage 3 times above the mean in any generated cluster.

Figure 6

Forest plots of (A) Diagnostic odds ratio, (B) Sensitivity, (C) Specificity, (D) negative predictive values for screening OPMD and oral mucosal cancerous lesions with influential outliers excluded.