【摘要】 目的 基于卷积神经网络的深度学习技术，迭代升级传统U-net算法，分析判读曲面体层片牙槽骨吸收严重程度，探究在牙周炎诊断中的价值。方法 构建Ali-U-Net算法，分析公共数据集116例脱敏口腔患者的全景X片图像，计算每颗牙的牙槽骨吸收程度，比较U-net和Faster RCNN等传统算法。结果 以高年资牙周专科医师的判读结果为对照，AliU-Net网络曲线下面积为0.93，高于原生U-Net(0.90)和Faster RCNN(0.67)。结论 基于人工智能卷积神经网络成功迭代建立了Ali-U-Net算法，在曲面体层片牙槽骨吸收严重程度判读方面体现了较好的应用价值。将本算法与病历信息的智能读取相结合或可应用于牙周炎的分期及分级智能诊断。更多还原

【Abstract】 Objective Based on the convolutional neural networks, aimed to construct a new U-net algorithm to detect the alveolar bone loss in panoramic radiographs and assist the periodontitis diagnosis. Methods AliU-Net was established and applied to a public dataset containing 116 oral panoramic radiographs, assessed the images for alveolar bone loss. The results were also compared with those of U-net and faster RCNN.Results The interpretation results of well-trained periodontologist were used as control, Ali-U-Net showed a great ability in recognition of alveolar bone loss. The receiver-operating characteristic curve showed that the area under the curve(AUC) of Ali-U-Net was 0.93, which was greater than that of other methods(AUC of U-Net and Faster RCNN was 0.90 and 0.67, respectively). Conclusion Based on the convolutional neural networks of deep learning algorithm, a novel Ali-U-Net algorithm was successfully constructed to detect the alveolar bone loss in panoramic radiographs and displayed a better diagnostic value for periodontitis.更多还原