利用深度学习算法检测全景x线片牙周骨丢失类型和功能缺损:一项回顾性研究

Sevda Kurt-Bayrakdar, İbrahim Şevki Bayrakdar, Muhammed Burak Yavuz, Nichal Sali, Özer Çelik, Oğuz Köse, Bilge Cansu Uzun Saylan, Batuhan Kuleli, Rohan Jagtap, Kaan Orhan
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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). 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引用次数: 0

摘要

摘要:本回顾性研究旨在开发一种用于全景x线片解释的深度学习算法,并研究该算法在检测水平牙槽骨丢失、垂直骨缺损和分形缺损等牙周问题中的性能。方法采用1121张全景式x线影像进行研究。采用分割法对上颌和下颌骨牙槽骨总缺损(2251例)、牙间骨缺损(25303例)和牙分叉缺损(2815例)进行标记。此外,牙间骨损失按缺损类型分为水平骨损失(n = 21839)和垂直骨损失(n = 3464)。采用U-Net架构,开发了基于卷积神经网络(CNN)的人工智能(AI)系统。通过混淆矩阵和ROC曲线分析对深度学习算法的性能进行统计评价。结果该系统对全牙槽骨缺损的诊断效能最高,对垂直骨缺损的诊断效能最低。全牙槽骨丢失的敏感性、精密度、F1评分、准确度、AUC值分别为1、0.995、0.997、0.994、0.951;水平骨损失分别为0.947、0.939、0.943、0.892、0.910;垂直骨缺损分别为0.558、0.846、0.673、0.506、0.733,分叉骨缺损分别为0.892、0.933、0.912、0.837、0.868。结论人工智能系统在确定牙周骨丢失模式和牙x线片功能缺陷方面具有良好的效果。这表明CNN算法还可以用于提供更详细的信息,如自动确定牙周病的严重程度和各种牙科x线片的治疗计划。
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Detection of Periodontal Bone Loss Types and Furcation Defects from Panoramic Radiographs Using Deep Learning Algorithm: A Retrospective Study
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.
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