基于深度学习的慢性阻塞性肺病综合早期诊断模型的开发与应用

IF 4.7 2区 医学 Q1 RESPIRATORY SYSTEM Respiratory Research Pub Date : 2024-04-18 DOI:10.1186/s12931-024-02793-3
Zecheng Zhu, Shunjin Zhao, Jiahui Li, Yuting Wang, Luopiao Xu, Yubing Jia, Zihan Li, Wenyuan Li, Gang Chen, Xifeng Wu
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引用次数: 0

摘要

慢性阻塞性肺病(COPD)是一种常诊断但可治疗的疾病,前提是能及早发现并进行有效管理。本研究旨在通过整合深度学习和放射组学特征,开发一种先进的慢性阻塞性肺病诊断模型。我们使用的数据集包括 2983 名参与者的 CT 图像,其中 2317 名参与者还通过问卷调查提供了流行病学数据。深度学习特征使用变异自动编码器提取,放射组学特征使用 PyRadiomics 软件包获取。多层感知器用于构建基于深度学习特征和放射组学特征的独立模型,以及两者的融合模型。随后,流行病学问卷数据也被纳入其中,以建立一个更全面的模型。使用准确度、精确度、召回率、F1分数、Brier分数、接收者操作特征曲线和曲线下面积(AUC)等指标对独立模型、融合模型和综合模型的诊断性能进行了评估和比较。融合模型表现突出,AUC 为 0.952,超过了仅基于深度学习特征(AUC = 0.844)或放射组学特征(AUC = 0.944)的独立模型。值得注意的是,结合了深度学习特征、放射组学特征和问卷变量的综合模型在所有模型中表现出最高的诊断性能,AUC 为 0.971。我们开发并实施了一种数据融合策略,以构建一种整合了深度学习特征、放射组学特征和问卷变量的最先进的慢性阻塞性肺病诊断模型。我们的数据融合策略被证明是有效的,而且该模型可以很容易地部署到临床环境中。不适用。本研究不是临床试验,不报告对人类参与者进行医疗干预的结果。
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Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease
Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.
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来源期刊
Respiratory Research
Respiratory Research 医学-呼吸系统
自引率
1.70%
发文量
314
期刊介绍: Respiratory Research publishes high-quality clinical and basic research, review and commentary articles on all aspects of respiratory medicine and related diseases. As the leading fully open access journal in the field, Respiratory Research provides an essential resource for pulmonologists, allergists, immunologists and other physicians, researchers, healthcare workers and medical students with worldwide dissemination of articles resulting in high visibility and generating international discussion. Topics of specific interest include asthma, chronic obstructive pulmonary disease, cystic fibrosis, genetics, infectious diseases, interstitial lung diseases, lung development, lung tumors, occupational and environmental factors, pulmonary circulation, pulmonary pharmacology and therapeutics, respiratory immunology, respiratory physiology, and sleep-related respiratory problems.
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