Apical periodontitis (AP) is one of the most prevalent disorders in dentistry. However, it can be underdiagnosed in asymptomatic patients. In addition, the perioperative evaluation of 3-dimensional (3D) lesion volume is of great clinical relevance, but the required slice-by-slice manual delineation method is time- and labor-intensive. Here, for quickly and accurately detecting and segmenting periapical lesions (PALs) associated with AP on cone beam computed tomography (CBCT) images, we proposed and geographically validated a novel 3D deep convolutional neural network algorithm, named PAL-Net. On the internal 5-fold cross-validation set, our PAL-Net achieved an area under the receiver operating characteristic curve (AUC) of 0.98. The algorithm also improved the diagnostic performance of dentists with varying levels of experience, as evidenced by their enhanced average AUC values (junior dentists: 0.89-0.94; senior dentists: 0.91-0.93), and significantly reduced the diagnostic time (junior dentists: 69.3 min faster; senior dentists: 32.4 min faster). Moreover, our PAL-Net achieved an average Dice similarity coefficient over 0.87 (0.85-0.88), which is superior or comparable to that of other existing state-of-the-art PAL segmentation algorithms. Furthermore, we validated the generalizability of the PAL-Net system using multiple external data sets from Central, East, and North China, showing that our PAL-Net has strong robustness. Our PAL-Net can help improve the diagnostic performance and speed of dentists working from CBCT images, provide clinically relevant volume information to dentists, and can potentially be applied in dental clinics, especially without expert-level dentists or radiologists.

中文翻译：

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通过 3D CNN 算法进行临床导向的 CBCT 根尖病变评估。

根尖周炎（AP）是牙科中最常见的疾病之一。然而，无症状患者可能会被漏诊。此外，围手术期评估三维（3D）病灶体积具有很大的临床意义，但所需的逐层手动勾画方法耗时耗力。在这里，为了快速准确地检测和分割锥束计算机断层扫描 (CBCT) 图像上与 AP 相关的根尖周病变 (PAL)，我们提出并在地理上验证了一种新颖的 3D 深度卷积神经网络算法，称为 PAL-Net。在内部 5 倍交叉验证集上，我们的 PAL-Net 实现了 0.98 的受试者工作特征曲线下面积 (AUC)。该算法还提高了不同经验水平牙医的诊断性能，其平均 AUC 值提高（初级牙医：0.89-0.94；高级牙医：0.91-0.93），并显着缩短了诊断时间（初级牙医：69.3）分钟快；高级牙医：快 32.4 分钟）。此外，我们的 PAL-Net 的平均 Dice 相似度系数超过 0.87 (0.85-0.88)，这优于或堪比其他现有最先进的 PAL 分割算法。此外，我们使用来自华中、华东和华北的多个外部数据集验证了 PAL-Net 系统的通用性，表明我们的 PAL-Net 具有很强的鲁棒性。我们的 PAL-Net 可以帮助牙医提高 CBCT 图像的诊断性能和速度，为牙医提供临床相关的体积信息，并且有可能应用于牙科诊所，特别是在没有专家级牙医或放射科医生的情况下。