Artificial intelligence in dental diagnosis: evaluating CNN models for caries and periapical lesions detection

Abstract

INTRODUCTION. Convolutional neural networks (CNNs) show strong promise for automating dental diagnosis from radiographic images. Robust head-to-head comparisons across tasks and datasets are needed to guide model selection for clinical use.

MATERIALS AND METHODS. We compared three lightweight CNNs – EfficientNet-B0, ResNet-18, and MobileNetV3 – for two classification tasks: enamel caries on intraoral images and periapical lesions on panoramic radiographs. Data comprised the Caries-Spectra dataset (2,000 intraoral images; advanced, earlystage, and no caries) and a panoramic radiograph set with 13,071 images labeled with periapical lesion scores (PAI 3–5). For the periapical task, data augmentation was applied to the panoramic training split only, increasing its size to 17,004 training instances; validation and test splits (as well as all Caries-Spectra splits) remained at their original sizes. Models were trained via transfer learning with early stopping and evaluated using accuracy, precision, recall, F1-score, and confusion matrices.

RESULTS. EfficientNet-B0 achieved the best overall performance on both tasks, reaching 99.74% accuracy for caries detection and 69.65% accuracy for periapical lesion detection, outperforming ResNet18 and MobileNetV3 across the reported metrics.

CONCLUSIONS. Lightweight CNNs – particularly EfficientNet-B0 – are effective for dental image classification and are suitable candidates for integration into clinical diagnostic workflows. Model architecture choice and data quality materially influence performance