卷积神经网络与分类算法相结合诊断牙周炎。

IF 1.6 3区 医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Oral Radiology Pub Date : 2024-07-01 Epub Date: 2024-02-23 DOI:10.1007/s11282-024-00739-5
Fang Dai, Qiangdong Liu, Yuchen Guo, Ruixiang Xie, Jingting Wu, Tian Deng, Hongbiao Zhu, Libin Deng, Li Song
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引用次数: 0

摘要

目标:我们旨在开发一种基于卷积神经网络(CNN)并结合分类算法(CA)的深度学习模型,以帮助牙医快速准确地诊断牙周炎的阶段:材料: 收集了根尖周X光片(PER)和临床数据。在 PER 上训练包括 Alexnet、VGG16 和 ResNet18 在内的 CNN,以建立无牙周骨质流失(PBL)和牙周骨质流失的 PER-CNN 模型。随机森林(RF)、支持向量机(SVM)、天真贝叶斯(NB)、逻辑回归(LR)和 k 最近邻(KNN)等 CA 被添加到 PER-CNN 模型中,用于对照、I 期、II 期和 III/IV 期牙周炎。使用梯度加权类激活映射法制作了热图,以直观显示 PER-Alexnet 模型的关注区域。根据十个 PER-CNN 分数和临床特征进行聚类分析:PER-Alexnet 和 PER-VGG16 模型的准确率分别为 0.872 和 0.853,其中 PER-Alexnet 和 PER-VGG16 模型的准确率更高。性能最高的 PER-Alexnet + RF 模型对对照组、I 期、II 期和 III/IV 期的准确率分别为 0.968、0.960、0.835 和 0.842。热图显示,该模型预测的感兴趣区是牙周炎骨病变。根据 PER-Alexnet 评分,我们发现年龄和吸烟与牙周炎有明显关系:结论:PER-Alexnet + RF 模型在全病例牙周诊断方面具有很高的性能。结论:PER-Alexnet + RF 模型在全病例牙周诊断方面具有很高的性能,CNN 模型与 CA 相结合可以帮助牙科医生快速准确地诊断牙周炎的阶段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.

Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.

Objectives: We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis.

Materials and methods: Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation mapping method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics.

Results: The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores.

Conclusion: The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis.

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来源期刊
Oral Radiology
Oral Radiology DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.20
自引率
13.60%
发文量
87
审稿时长
>12 weeks
期刊介绍: As the official English-language journal of the Japanese Society for Oral and Maxillofacial Radiology and the Asian Academy of Oral and Maxillofacial Radiology, Oral Radiology is intended to be a forum for international collaboration in head and neck diagnostic imaging and all related fields. Oral Radiology features cutting-edge research papers, review articles, case reports, and technical notes from both the clinical and experimental fields. As membership in the Society is not a prerequisite, contributions are welcome from researchers and clinicians worldwide.
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