Diagnostic performance of deep learning models in classifying mandibular third molar and mandibular canal contact status on panoramic radiographs: A systematic review and meta-analysis.

Abstract

Purpose: Panoramic radiographs have recently become a platform for deep learning models, which show potential in enhancing diagnostic accuracy for detecting contact between mandibular third molars and the mandibular canal. However, detailed information regarding the accuracy of these models in identifying such contact remains limited.

Materials and methods: In accordance with the PRISMA-2020 and PRISMA-DTA guidelines, the PubMed, ScienceDirect, Web of Science, Embase, and EBSCO databases were systematically searched up to September 2024. Eligible studies employed deep learning models based on convolutional neural networks to classify the contact between mandibular third molars and the mandibular canal. Extracted metrics included accuracy, sensitivity, specificity, precision, and F1-score. A meta-analysis using random effects models pooled these performance metrics, while univariate and multivariate meta-regressions were conducted to explore sources of heterogeneity. Study quality was assessed using the QUADAS-2 tool.

Results: Seven studies incorporating 4,955 panoramic radiographs reported pooled performance metrics of 83.4% accuracy, 80.2% sensitivity, 85.8% specificity, 83.3% precision, and an F1-score of 80.9%. High heterogeneity (I² > 90%) was primarily attributable to variations in sample size, image resolution, model architecture, and model complexity. Meta-regression analyses identified image resolution and architecture (e.g., VGG-16, AlexNet) as key factors. Although the overall risk of bias was low, the patient selection domain was often unclear.

Conclusion: Deep learning models exhibit significant promise in evaluating mandibular third molar and mandibular canal contact on panoramic radiographs, potentially complementing traditional methods. The adoption of standardized protocols, diverse datasets, and explainable artificial intelligence will be crucial for broader clinical application.