Affiliation:
1. Eskisehir Osmangazi University
2. Recep Tayyip Erdogan University
3. Dokuz Eylül University
4. University of Mississippi Medical Center School of Dentistry
5. Ankara University
Abstract
Abstract
Background
This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of some periodontal problems such as horizontal alveolar bone loss, vertical bone defect, and furcation defect.
Methods
A total of 1121 panoramic radiographic images were used in this study. Total alveolar bone losses in the maxilla and mandibula (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect types. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis.
Results
The system showed the highest diagnostic performance in the detection of total alveolar bone losses and the lowest in the detection of vertical bone defects. The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone defects and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively).
Conclusions
AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.
Publisher
Research Square Platform LLC
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