An artificial intelligence grading system of apical periodontitis in cone-beam computed tomography data.

IF 2.9 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE
Tianyin Zhao, Huili Wu, Diya Leng, Enhui Yao, Shuyun Gu, Minhui Yao, Qinyu Zhang, Tong Wang, Daming Wu, Lizhe Xie
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引用次数: 0

Abstract

Objectives: In order to assist junior doctors in better diagnosing apical periodontitis (AP), an artificial intelligence AP grading system was developed based on deep learning (DL) and its reliability and accuracy were evaluated.

Methods: One hundred and twenty cone-beam computed tomography (CBCT) images were selected to construct a classification dataset with four categories, which were divided by CBCT periapical index (CBCTPAI), including normal periapical tissue, CBCTPAI 1-2, CBCTPAI 3-5, and young permanent teeth. Three classic algorithms (ResNet50/101/152) as well as one self-invented algorithm (PAINet) were compared with each other. PAINet were also compared with two recent Transformer-based models and three attention models. Their performance was evaluated by accuracy, precision, recall, balanced F score (F1-score), and the area under the macro-average receiver operating curve (AUC). Reliability was evaluated by Cohen's kappa to compare the consistency of model predicted labels with expert opinions.

Results: PAINet performed best among the four algorithms. The accuracy, precision, recall, F1-score, and AUC on the test set were 0.9333, 0.9415, 0.9333, 0.9336, and 0.9972, respectively. Cohen's kappa was 0.911, which represented almost perfect consistency.

Conclusions: PAINet can accurately distinguish between normal periapical tissues, CBCTPAI 1-2, CBCTPAI 3-5, and young permanent teeth. Its results were highly consistent with expert opinions. It can help junior doctors diagnose and score AP, reducing the burden. It can also be promoted in areas where experts are lacking to provide professional diagnostic opinions.

锥束计算机断层扫描数据中根尖牙周炎的人工智能分级系统。
目的:方法:选取120张锥束计算机断层扫描(CBCT)图像构建分类数据集,按CBCT根尖周指数(CBCTPAI)分为正常根尖周组织、CBCTPAI 1-2、CBCTPAI 3-5和年轻恒牙四类。对三种经典算法(ResNet50/101/152)和一种自创算法(PAINet)进行了比较。PAINet 还与两种最新的基于 Transformer 的模型和三种注意力模型进行了比较。它们的性能通过准确度、精确度、召回率、平衡 F 分数(F1 分数)和宏观平均接收器工作曲线下面积(AUC)进行评估。可靠性通过科恩卡帕进行评估,以比较模型预测标签与专家意见的一致性:结果:PAINet 在四种算法中表现最佳。测试集上的准确度、精确度、召回率、F1 分数和 AUC 分别为 0.9333、0.9415、0.9333、0.9336 和 0.9972。科恩卡帕值为 0.911,几乎完全一致:PAINet能准确区分正常根尖周组织、CBCTPAI 1-2、CBCTPAI 3-5和年轻恒牙。其结果与专家意见高度一致。它可以帮助初级医生诊断和评分 AP,减轻他们的负担。它还可以在缺乏专家提供专业诊断意见的地区进行推广。
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来源期刊
CiteScore
5.60
自引率
9.10%
发文量
65
审稿时长
4-8 weeks
期刊介绍: Dentomaxillofacial Radiology (DMFR) is the journal of the International Association of Dentomaxillofacial Radiology (IADMFR) and covers the closely related fields of oral radiology and head and neck imaging. Established in 1972, DMFR is a key resource keeping dentists, radiologists and clinicians and scientists with an interest in Head and Neck imaging abreast of important research and developments in oral and maxillofacial radiology. The DMFR editorial board features a panel of international experts including Editor-in-Chief Professor Ralf Schulze. Our editorial board provide their expertise and guidance in shaping the content and direction of the journal. Quick Facts: - 2015 Impact Factor - 1.919 - Receipt to first decision - average of 3 weeks - Acceptance to online publication - average of 3 weeks - Open access option - ISSN: 0250-832X - eISSN: 1476-542X
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