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<p>Artificial intelligence is increasingly explored in dentistry to improve workflow efficiency and support image-based analysis. This study benchmarks deep learning (DL) and machine learning (ML) approaches for classifying pediatric dental views using a publicly available dataset of 9,562 intraoral images from children aged 1–14 years, covering eight maxillary and mandibular view classes. Under 10-fold cross-validation, MobileNetV2 achieved the highest performance among DL models (accuracy 95.18%, F1-score 0.95, AUC 0.997), followed by InceptionV3 (93.76%) and Xception (93.07%). Among ML methods, Logistic Regression achieved 93.33% accuracy with an AUC of 0.996. A symmetry-aware architecture, DentSym, was further proposed, achieving an average accuracy of 98.92% with balanced precision, recall, and F1-score. Model interpretability was examined using Grad-CAM, indicating that predictions were based on relevant dental regions. The highest-performing model was integrated into a prototype iOS application for real-time classification as a proof of concept. However, as cross-validation was performed at the image level due to the absence of patient identifiers, the reported performance should be interpreted as an upper-bound estimate under the current experimental setting. The study provides baseline reference results for this dataset and highlights the potential of explainable, mobile-based AI systems for future dental applications.</p>

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Automated classification of maxillary and mandibular dental views on intraoral photographs: a comparative benchmark study and mobile proof-of-concept

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Elham Tahsin Yasin,
  • Murat Koklu,
  • Mohannad Alkanan,
  • Yonis Gulzar

摘要

Artificial intelligence is increasingly explored in dentistry to improve workflow efficiency and support image-based analysis. This study benchmarks deep learning (DL) and machine learning (ML) approaches for classifying pediatric dental views using a publicly available dataset of 9,562 intraoral images from children aged 1–14 years, covering eight maxillary and mandibular view classes. Under 10-fold cross-validation, MobileNetV2 achieved the highest performance among DL models (accuracy 95.18%, F1-score 0.95, AUC 0.997), followed by InceptionV3 (93.76%) and Xception (93.07%). Among ML methods, Logistic Regression achieved 93.33% accuracy with an AUC of 0.996. A symmetry-aware architecture, DentSym, was further proposed, achieving an average accuracy of 98.92% with balanced precision, recall, and F1-score. Model interpretability was examined using Grad-CAM, indicating that predictions were based on relevant dental regions. The highest-performing model was integrated into a prototype iOS application for real-time classification as a proof of concept. However, as cross-validation was performed at the image level due to the absence of patient identifiers, the reported performance should be interpreted as an upper-bound estimate under the current experimental setting. The study provides baseline reference results for this dataset and highlights the potential of explainable, mobile-based AI systems for future dental applications.