AIM To verify the diagnostic performance of an artificial intelligence system based on the deep convolutional neural network method to detect periapical pathosis on cone-beam computed tomography (CBCT) images. METHODOLOGY images of 153 periapical lesions obtained from 109 patients were included. The specific area of the jaw and teeth associated with the periapical lesions were then determined by a human observer. Lesion volumes were calculated using the manual segmentation methods using Fujifilm-Synapse 3D software (Fujifilm Medical Systems, Tokyo, Japan). The neural network was then used to determine (i) whether the lesion could be detected; (ii) if the lesion was detected, where it was localized (maxilla, mandible or specific tooth); and (iii) lesion volume. Manual segmentation and artificial intelligence (AI) (Diagnocat Inc., San Francisco, CA, USA) methods were compared using Wilcoxon signed rank test and Bland-Altman analysis. RESULTS The deep convolutional neural network system was successful in detecting teeth and numbering specific teeth. Only one tooth was incorrectly identified. The AI system was able to detect 142 of a total of 153 periapical lesions. The reliability of correctly detecting a periapical lesion was 92.8%. The deep convolutional neural network volumetric measurements of the lesions were similar to those with manual segmentation. There was no significant difference between the two measurement methods (P > 0.05). CONCLUSIONS Volume measurements performed by humans and by AI systems were comparable to each other. AI systems based on deep learning methods can be useful for detecting periapical pathosis on CBCT images for clinical application.

中文翻译：

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评估用于在锥形束计算机断层扫描中检测根尖周病变的人工智能。

目的验证基于深度卷积神经网络方法在锥束计算机断层扫描（CBCT）图像上检测根尖病变的人工智能系统的诊断性能。方法学图像包括109例患者的153例根尖周病变。然后由人类观察者确定与根尖周病变相关的颌骨和牙齿的特定区域。使用Fujifilm-Synapse 3D软件（Fujifilm Medical Systems，东京，日本）使用手动分割方法计算病变体积。然后使用神经网络来确定（i）是否可以检测到病变；（ii）如果发现病变，则定位在何处（上颌骨，下颌骨或特定牙齿）；（iii）病变体积。手动分割和人工智能（AI）（Diagnocat Inc.，San Francisco，CA，使用Wilcoxon符号秩检验和Bland-Altman分析比较了美国的方法。结果深度卷积神经网络系统成功地检测出牙齿并为特定牙齿编号。仅错误地识别了一颗牙齿。AI系统能够检测到总共153个根尖周病变中的142个。正确检测根尖周病变的可靠性为92.8％。病变的深层卷积神经网络体积测量与手动分割相似。两种测量方法之间无显着差异（P> 0.05）。结论由人类和AI系统进行的体积测量可相互比较。基于深度学习方法的AI系统可用于检测CBCT图像上的根尖周病变，以用于临床应用。