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CC BY 4.0 · Eur J Dent 2025; 19(03): 721-728
DOI: 10.1055/s-0044-1791784
Original Article

Classifying Three-Wall Intrabony Defects from Intraoral Radiographs Using Deep Learning–Based Convolutional Neural Network Models

Authors

  • Kanteera Piroonsan

    1   Department of Oral Medicine and Periodontology, Faculty of Dentistry, Mahidol University, Bangkok, Thailand

  • Kununya Pimolbutr

    1   Department of Oral Medicine and Periodontology, Faculty of Dentistry, Mahidol University, Bangkok, Thailand

  • Kallapat Tansriratanawong 

    1   Department of Oral Medicine and Periodontology, Faculty of Dentistry, Mahidol University, Bangkok, Thailand
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Abstract

Objective Intraoral radiographs are used in periodontal therapy to understand interdental bony health and defects. However, identifying three-wall bony defects is challenging due to their variations. Therefore, this study aimed to classify three-wall intrabony defects using deep learning–based convolutional neural network (CNN) models to distinguish between three-wall and non-three-wall bony defects via intraoral radiographs.

Materials and Methods A total of 1,369 radiographs were obtained from 556 patients who had undergone periodontal surgery. These radiographs, each featuring at least one area of intrabony defect, were categorized into 15 datasets based on the presence of three-wall or non-three-wall intrabony defects. We then trained six CNN models—InceptionV3, InceptionResNetV2, ResNet50V2, MobileNetV3Large, EfficientNetV2B1, and VGG19—using these datasets. Model performance was assessed based on the area under curve (AUC), with an AUC value ≥ 0.7 considered acceptable. Various metrics were thoroughly examined, including accuracy, precision, recall, specificity, negative predictive value (NPV), and F1 score.

Results In datasets excluding circumferential defects from bitewing radiographs, InceptionResNetV2, ResNet50V2, MobileNetV3Large, and VGG19 achieved AUC values of 0.70, 0.73, 0.77, and 0.75, respectively. Among these models, the VGG19 model exhibited the best performance, with an accuracy of 0.75, precision of 0.78, recall of 0.82, specificity of 0.67, NPV of 0.88, and an F1 score of 0.75.

Conclusion The CNN models used in the study showed an AUC value of 0.7 to 0.77 for classifying three-wall intrabony defects. These values demonstrate the potential clinical application of this approach for periodontal examination, diagnosis, and treatment planning for periodontal surgery.

Keywords

deep learning - machine learning - neural networks - artificial intelligence - periodontal bone loss - intrabony defect


Publication History

Article published online:
21 November 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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