Abstract：
OBJECTIVE: This study aims to assess the diagnostic accuracy of an artificial intelligence (AI) system employing deep learning for identifying dental plaque, utilizing a dataset comprising photographs of permanent teeth.
METHODS: In this study, photographs of 168 teeth belonging to 20 patients aged between 10 and 15 years, who met our criteria, were included. Intraoral photographs were taken of the patients in two stages, before and after the application of the plaque staining agent. To train the AI system to identify plaque on teeth with dental plaque that is not discolored, plaque and teeth were marked on photos with exposed dental plaque. One hundred forty teeth were used to construct the training group, while 28 teeth were used to create the test group. Another dentist reviewed images of teeth with dental plaque that was not discolored, and the effectiveness of AI in detecting plaque was evaluated using pertinent performance indicators. To compare the AI model and the dentist\'s evaluation outcomes, the mean intersection over union (IoU) values were evaluated by the Wilcoxon test.
RESULTS: The AI system showed higher performance in our study with a precision of 82% accuracy, 84% sensitivity, 83% F1 score, 87% accuracy, and 89% specificity in plaque detection. The area under the curve (AUC) value was found to be 0.922, and the IoU value was 76%. Subsequently, the dentist\'s plaque diagnosis performance was also evaluated. The IoU value was 0.71, and the AUC was 0.833. The AI model showed statistically significantly higher performance than the dentist (P < 0.05).
CONCLUSIONS: The AI algorithm that we developed has achieved promising results and demonstrated clinically acceptable performance in detecting dental plaque compared to a dentist.
METHODS: In this study, photographs of 168 teeth belonging to 20 patients aged between 10 and 15 years, who met our criteria, were included. Intraoral photographs were taken of the patients in two stages, before and after the application of the plaque staining agent. To train the AI system to identify plaque on teeth with dental plaque that is not discolored, plaque and teeth were marked on photos with exposed dental plaque. One hundred forty teeth were used to construct the training group, while 28 teeth were used to create the test group. Another dentist reviewed images of teeth with dental plaque that was not discolored, and the effectiveness of AI in detecting plaque was evaluated using pertinent performance indicators. To compare the AI model and the dentist\'s evaluation outcomes, the mean intersection over union (IoU) values were evaluated by the Wilcoxon test.
RESULTS: The AI system showed higher performance in our study with a precision of 82% accuracy, 84% sensitivity, 83% F1 score, 87% accuracy, and 89% specificity in plaque detection. The area under the curve (AUC) value was found to be 0.922, and the IoU value was 76%. Subsequently, the dentist\'s plaque diagnosis performance was also evaluated. The IoU value was 0.71, and the AUC was 0.833. The AI model showed statistically significantly higher performance than the dentist (P < 0.05).
CONCLUSIONS: The AI algorithm that we developed has achieved promising results and demonstrated clinically acceptable performance in detecting dental plaque compared to a dentist.
摘要：
目的：本研究旨在评估使用深度学习识别牙菌斑的人工智能（AI）系统的诊断准确性。利用包含恒牙照片的数据集。
方法：在本研究中，20名年龄在10至15岁之间的患者的168颗牙齿的照片，符合我们标准的人，包括在内。分两个阶段拍摄了患者的口内照片，在应用斑块染色剂之前和之后。为了训练人工智能系统识别牙齿上的牙菌斑，牙菌斑没有变色，在暴露牙菌斑的照片上标记牙菌斑和牙齿。一百四十颗牙齿被用来建造训练组，同时使用28颗牙齿创建测试组。另一位牙医检查了牙齿的牙菌斑没有变色的图像，并使用相关性能指标评估AI检测斑块的有效性。要比较AI模型和牙医的评估结果，通过Wilcoxon检验评估平均交集联合(IoU)值。
结果：AI系统在我们的研究中显示出更高的性能，精度为82％，84%灵敏度,83%F1得分，87%的准确度，在斑块检测中的特异性为89%。曲线下面积(AUC)值为0.922,IoU值为76%。随后，还评估了牙医的牙菌斑诊断表现。IoU值为0.71,AUC为0.833。AI模型显示出统计学上明显高于牙医（P<0.05）。
结论：与牙医相比，我们开发的AI算法取得了有希望的结果，并证明了临床上可接受的牙菌斑检测性能。
方法：在本研究中，20名年龄在10至15岁之间的患者的168颗牙齿的照片，符合我们标准的人，包括在内。分两个阶段拍摄了患者的口内照片，在应用斑块染色剂之前和之后。为了训练人工智能系统识别牙齿上的牙菌斑，牙菌斑没有变色，在暴露牙菌斑的照片上标记牙菌斑和牙齿。一百四十颗牙齿被用来建造训练组，同时使用28颗牙齿创建测试组。另一位牙医检查了牙齿的牙菌斑没有变色的图像，并使用相关性能指标评估AI检测斑块的有效性。要比较AI模型和牙医的评估结果，通过Wilcoxon检验评估平均交集联合(IoU)值。
结果：AI系统在我们的研究中显示出更高的性能，精度为82％，84%灵敏度,83%F1得分，87%的准确度，在斑块检测中的特异性为89%。曲线下面积(AUC)值为0.922,IoU值为76%。随后，还评估了牙医的牙菌斑诊断表现。IoU值为0.71,AUC为0.833。AI模型显示出统计学上明显高于牙医（P<0.05）。
结论：与牙医相比，我们开发的AI算法取得了有希望的结果，并证明了临床上可接受的牙菌斑检测性能。
