利用多通道 ResNet 对计算机断层扫描进行端到端 CRSwNP 预测。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2024-06-06 eCollection Date: 2024-01-01 DOI:10.1155/2024/4960630
Shixin Lai, Weipiao Kang, Yaowen Chen, Jisheng Zou, Siqi Wang, Xuan Zhang, Xiaolei Zhang, Yu Lin
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引用次数: 0

摘要

慢性鼻炎(CRS)是一种全球性疾病,其特点是治疗效果差、复发率高,严重影响患者的生活质量。由于其复杂的病理生理学和多样的临床表现,CRS 被分为多种亚型,以便于进行更精确的诊断、治疗和预后预测。其中,伴有鼻息肉的 CRS(CRSwNP)又分为嗜酸性 CRSwNP(eCRSwNP)和非嗜酸性 CRSwNP(non-eCRSwNP)。然而,目前还缺乏精确的预测性诊断和治疗方法,因此研究 CRSwNP 内型的精确诊断技术对于实现 CRSwNP 的精准医疗至关重要。本文提出了一种利用多角度鼻窦计算机断层扫描(CT)图像结合人工智能(AI)预测 CRSwNP 内型的方法,以区分 eCRSwNP 和非 eCRSwNP 患者。所考虑的数据集包括来自 192 名 CRSwNP 患者的 22,265 张 CT 图像,其中 13,203 张来自非 eCRSwNP 患者,9,062 张来自 eCRSwNP 患者。网络模型的测试结果表明,多角度图像能为网络提供更多有用信息,准确率达到 98.43%,精确率达到 98.1%,召回率达到 98.1%,特异性达到 98.7%,AUC 值达到 0.984。与单通道神经网络有限的学习能力相比,我们提出的多通道特征自适应融合模型能捕捉多尺度空间特征,提高模型对CT图像中关键窦道信息的关注度,从而最大限度地提高检测准确率。这种基于深度学习的 CRSwNP 内型诊断模型具有出色的分类性能,为治疗前准确预测 CRSwNP 内型提供了一种无创方法,为新时代的 CRSwNP 精准医疗铺平了道路。
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An End-to-End CRSwNP Prediction with Multichannel ResNet on Computed Tomography.

Chronic rhinosinusitis (CRS) is a global disease characterized by poor treatment outcomes and high recurrence rates, significantly affecting patients' quality of life. Due to its complex pathophysiology and diverse clinical presentations, CRS is categorized into various subtypes to facilitate more precise diagnosis, treatment, and prognosis prediction. Among these, CRS with nasal polyps (CRSwNP) is further divided into eosinophilic CRSwNP (eCRSwNP) and noneosinophilic CRSwNP (non-eCRSwNP). However, there is a lack of precise predictive diagnostic and treatment methods, making research into accurate diagnostic techniques for CRSwNP endotypes crucial for achieving precision medicine in CRSwNP. This paper proposes a method using multiangle sinus computed tomography (CT) images combined with artificial intelligence (AI) to predict CRSwNP endotypes, distinguishing between patients with eCRSwNP and non-eCRSwNP. The considered dataset comprises 22,265 CT images from 192 CRSwNP patients, including 13,203 images from non-eCRSwNP patients and 9,062 images from eCRSwNP patients. Test results from the network model demonstrate that multiangle images provide more useful information for the network, achieving an accuracy of 98.43%, precision of 98.1%, recall of 98.1%, specificity of 98.7%, and an AUC value of 0.984. Compared to the limited learning capacity of single-channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model's focus on crucial sinus information within the CT images to maximize detection accuracy. This deep learning-based diagnostic model for CRSwNP endotypes offers excellent classification performance, providing a noninvasive method for accurately predicting CRSwNP endotypes before treatment and paving the way for precision medicine in the new era of CRSwNP.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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