Classification of mandibular molar furcation involvement in periapical radiographs by deep learning.

IF 1.7 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Imaging Science in Dentistry Pub Date : 2024-09-01 Epub Date: 2024-08-12 DOI:10.5624/isd.20240020
Katerina Vilkomir, Cody Phen, Fiondra Baldwin, Jared Cole, Nic Herndon, Wenjian Zhang
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引用次数: 0

Abstract

Purpose: The purpose of this study was to classify mandibular molar furcation involvement (FI) in periapical radiographs using a deep learning algorithm.

Materials and methods: Full mouth series taken at East Carolina University School of Dental Medicine from 2011-2023 were screened. Diagnostic-quality mandibular premolar and molar periapical radiographs with healthy or FI mandibular molars were included. The radiographs were cropped into individual molar images, annotated as " healthy" or " FI," and divided into training, validation, and testing datasets. The images were preprocessed by PyTorch transformations. ResNet-18, a convolutional neural network model, was refined using the PyTorch deep learning framework for the specific imaging classification task. CrossEntropyLoss and the AdamW optimizer were employed for loss function training and optimizing the learning rate, respectively. The images were loaded by PyTorch DataLoader for efficiency. The performance of ResNet-18 algorithm was evaluated with multiple metrics, including training and validation losses, confusion matrix, accuracy, sensitivity, specificity, the receiver operating characteristic (ROC) curve, and the area under the ROC curve.

Results: After adequate training, ResNet-18 classified healthy vs. FI molars in the testing set with an accuracy of 96.47%, indicating its suitability for image classification.

Conclusion: The deep learning algorithm developed in this study was shown to be promising for classifying mandibular molar FI. It could serve as a valuable supplemental tool for detecting and managing periodontal diseases.

通过深度学习对根尖周X光片中下颌臼齿毛面受累情况进行分类。
目的:本研究的目的是使用深度学习算法对根尖周X光片中的下颌臼齿沟受累(FI)进行分类:筛选了 2011-2023 年间在东卡罗莱纳大学牙医学院拍摄的全口系列照片。包括下颌前磨牙和臼齿根尖周炎健康或FI的诊断质量X光片。X光片被裁剪成单个臼齿图像,标注为 "健康 "或 "FI",并分为训练、验证和测试数据集。图像经过 PyTorch 转换预处理。针对特定的成像分类任务,使用 PyTorch 深度学习框架改进了卷积神经网络模型 ResNet-18。在损失函数训练和优化学习率时,分别使用了 CrossEntropyLoss 和 AdamW 优化器。图像由 PyTorch DataLoader 加载,以提高效率。ResNet-18 算法的性能通过多个指标进行评估,包括训练和验证损失、混淆矩阵、准确性、灵敏度、特异性、接收者操作特征曲线(ROC)和 ROC 曲线下面积:经过充分训练后,ResNet-18 在测试集中对健康与 FI 磨牙进行了分类,准确率为 96.47%,表明其适合图像分类:结论:本研究中开发的深度学习算法在下颌磨牙FI分类方面很有前景。结论:本研究开发的深度学习算法在下颌臼齿FI分类方面具有良好的前景,可作为检测和管理牙周疾病的重要补充工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Imaging Science in Dentistry
Imaging Science in Dentistry DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
2.90
自引率
11.10%
发文量
42
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