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Volume 13
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10.3390/diagnostics13101800
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Article

Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study

by
Bilge Cansu Uzun Saylan
1,*,
Oğuzhan Baydar
2,
Esra Yeşilova
3,
Sevda Kurt Bayrakdar
4,
Elif Bilgir
3,
İbrahim Şevki Bayrakdar
3,
Özer Çelik
5 and
Kaan Orhan
6
1
Department of Periodontology, Faculty of Dentistry, Dokuz Eylul University, İzmir 35220, Turkey
2
Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ege University, İzmir 35040, Turkey
3
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
4
Department of Periodontology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
5
Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir 26480, Turkey
6
Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06830, Turkey
*
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(10), 1800; https://doi.org/10.3390/diagnostics13101800
Submission received: 23 January 2023 / Revised: 13 April 2023 / Accepted: 16 May 2023 / Published: 19 May 2023
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Abstract

The assessment of alveolar bone loss, a crucial element of the periodontium, plays a vital role in the diagnosis of periodontitis and the prognosis of the disease. In dentistry, artificial intelligence (AI) applications have demonstrated practical and efficient diagnostic capabilities, leveraging machine learning and cognitive problem-solving functions that mimic human abilities. This study aims to evaluate the effectiveness of AI models in identifying alveolar bone loss as present or absent across different regions. To achieve this goal, alveolar bone loss models were generated using the PyTorch-based YOLO-v5 model implemented via CranioCatch software, detecting periodontal bone loss areas and labeling them using the segmentation method on 685 panoramic radiographs. Besides general evaluation, models were grouped according to subregions (incisors, canines, premolars, and molars) to provide a targeted evaluation. Our findings reveal that the lowest sensitivity and F1 score values were associated with total alveolar bone loss, while the highest values were observed in the maxillary incisor region. It shows that artificial intelligence has a high potential in analytical studies evaluating periodontal bone loss situations. Considering the limited amount of data, it is predicted that this success will increase with the provision of machine learning by using a more comprehensive data set in further studies.
Keywords: alveolar bone loss; artificial intelligence; panoramic radiography; deep learning; segmentation alveolar bone loss; artificial intelligence; panoramic radiography; deep learning; segmentation

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MDPI and ACS Style

Uzun Saylan, B.C.; Baydar, O.; Yeşilova, E.; Kurt Bayrakdar, S.; Bilgir, E.; Bayrakdar, İ.Ş.; Çelik, Ö.; Orhan, K. Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study. Diagnostics 2023, 13, 1800. https://doi.org/10.3390/diagnostics13101800

AMA Style

Uzun Saylan BC, Baydar O, Yeşilova E, Kurt Bayrakdar S, Bilgir E, Bayrakdar İŞ, Çelik Ö, Orhan K. Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study. Diagnostics. 2023; 13(10):1800. https://doi.org/10.3390/diagnostics13101800

Chicago/Turabian Style

Uzun Saylan, Bilge Cansu, Oğuzhan Baydar, Esra Yeşilova, Sevda Kurt Bayrakdar, Elif Bilgir, İbrahim Şevki Bayrakdar, Özer Çelik, and Kaan Orhan. 2023. "Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study" Diagnostics 13, no. 10: 1800. https://doi.org/10.3390/diagnostics13101800

APA Style

Uzun Saylan, B. C., Baydar, O., Yeşilova, E., Kurt Bayrakdar, S., Bilgir, E., Bayrakdar, İ. Ş., Çelik, Ö., & Orhan, K. (2023). Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study. Diagnostics, 13(10), 1800. https://doi.org/10.3390/diagnostics13101800

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