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全景片下颌第三磨牙与下牙槽神经管定位的人工智能。

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography.

机构信息

Department of Oral Medicine and Oral Diagnosis, School of Dentistry, Seoul National University, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.

Department of Industrial and Systems Engineering, Dongguk University - Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul, 04620, Republic of Korea.

出版信息

Sci Rep. 2022 Feb 14;12(1):2456. doi: 10.1038/s41598-022-06483-2.

Abstract

Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen's kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.

摘要

确定下颌第三磨牙(M3)与下牙槽神经(IAN)的确切位置关系对于手术拔牙非常重要。全景放射摄影是最常见的牙科影像学检查。本研究的目的是开发一种人工智能(AI)模型,以确定在全景放射照片中 M3 和 IAN 重叠时它们之间的两种位置关系(真实接触和颊舌位置),并比较其性能与口腔颌面外科(OMFS)专家的性能。本研究共使用了 394 名患者的 571 张 M3 全景图像。其中,202 张图像分类为真实接触,246 张为密切接触,61 张为 IAN 颊侧位置,62 张为 IAN 舌侧位置。使用 ResNet-50 架构的深度卷积神经网络模型为每个任务进行了训练。我们将数据集随机分为 75%用于训练和验证,25%用于测试。在颊舌位置确定方面,模型性能优于真实接触位置确定(准确性 0.76、精度 0.83、召回率 0.67 和 F1 分数 0.73)(准确性 0.63、精度 0.62、召回率 0.63 和 F1 分数 0.61)。与 OMFS 专家相比,AI 在这两种位置确定中都表现出更高的准确性。在确定真实接触位置方面,OMFS 专家的准确率为 52.68%至 69.64%,而 AI 的准确率为 72.32%。在确定颊舌位置方面,OMFS 专家的准确率为 32.26%至 48.39%,而 AI 的准确率为 80.65%。此外,AI 之间的 Cohen's kappa 表现出相当大的一致性(0.61),而 OMFS 专家在颊舌位置确定方面的一致性较差。使用 AI 确定 M3 和 IAN 之间的位置关系是可行的,尤其是在颊舌定位方面。该模型可用于支持临床医生在 M3 治疗决策过程中的决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae4d/8844031/069c9910391d/41598_2022_6483_Fig1_HTML.jpg

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