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AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach

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Abstract

Tooth decay is a common oral disease worldwide, but errors in diagnosis can often be made in dental clinics, which can lead to a delay in treatment. This study aims to use artificial intelligence (AI) for the automated detection and localization of secondary, occlusal, and interproximal (D1, D2, D3) caries types on bite-wing radiographs. The eight hundred and sixty bite-wing radiographs were collected from the School of Dentistry database. Pre-processing and data augmentation operations were performed. Interproximal (D1, D2, D3), secondary, and occlusal caries on bite-wing radiographs were annotated by two oral radiologists. The data were split into 80% for training, 10% for validation, and 10% for testing. The AI-based training process was conducted using the YOLOv8 algorithm. A clinical decision support system interface was designed using the Python PyQT5 library, allowing for the use of dental caries detection without the need for complex programming procedures. In the test images, the average precision, average sensitivity, and average F1 score values for secondary, occlusal, and interproximal caries were obtained as 0.977, 0.932, and 0.954, respectively. The AI-based dental caries detection system yielded highly successful results in the test, receiving full approval from dentists for clinical use. YOLOv8 has the potential to increase sensitivity and reliability while reducing the burden on dentists and can prevent diagnostic errors in dental clinics.

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Authors and Affiliations

  1. Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Necmettin Erbakan University, Konya, Turkey

    Rabia Karakuş & Melek Tassoker

  2. Faculty of Technology, Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey

    Muhammet Üsame Öziç

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  1. Rabia Karakuş
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Contributions

All authors contributed to the study’s conception and design. RK and MT contributed to data collection, annotating, and evaluation of results. MÜÖ contributed to algorithm design, coding, and determination of performance evaluation metrics. All authors contributed equally to article manuscript writing, literature review, and article design.

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Correspondence to Muhammet Üsame Öziç.

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This study was conducted at the Faculty of Dentistry, Necmettin Erbakan University, Department of Dentomaxillofacial Radiology, with the approval of the Ethics Committee (dated 30 June 2022 and numbered 12–94) and was performed according to the stipulations laid out by the Declaration of Helsinki.

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It is a retrospective study.

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Karakuş, R., Öziç, M.Ü. & Tassoker, M. AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01113-x

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