应用人工智能分析胸部x线图像中COVID-19的感染指数和严重程度

Eduardo Garea-Llano, Eduardo Martinez Montes, Evelio Gonzalez Dalmaus
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引用次数: 0

摘要

Covid-19大流行导致强化治疗拥挤,使每个人都无法获得全职放射治疗服务。根据患者肺部的受累程度和胸部x线图像(CXR)的严重程度,强化医生需要一个指标来评估疾病晚期患者的进展情况。我们提出了一种算法来对诊断为COVID-19的晚期患者的CXR图像中肺部的影响进行分级。该算法将图像质量评估、数字图像处理和深度学习相结合,对肺组织进行分割和分类。本文提出的分割方法能够处理COVID-19重症或危重患者CXR图像中肺边界弥散的问题。拟合指数(IAF)的计算包括通过建立每一类像素数之间的关系对分割后的图像进行分类。CXR图像中肺假体的IAF指数及其计算算法。在IAF和放射科医师建立的严重程度国际分类之间建立了相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Affectation index and severity degree by COVID-19 in Chest X-ray images using artificial intelligence
The Covid-19 pandemic has caused the congestion of intensive therapies making it impossible for each to have a full-time radiology service. An indicator is necessary to allow intensivists to evaluate the evolution of patients in advanced state of the disease depending on the degree of involvement of their lungs and their severity in chest X-ray images (CXR). We propose an algorithm to grade the affectation of lungs in CXR images in patients diagnosed with COVID-19 in advanced state of the disease. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. The proposed segmentation method is capable of dealing with the problem of diffuse lung borders in CXR images of patients with COVID-19 severe or critical. The calculation of the affectation index (IAF) consists of the classification of the segmented image by establishing the relationship between the number of pixels of each class. The IAF index of lung affectation in CXR images and the algorithm for its calculation. A correlation was established between the IAF and the international classification of the degree of severity established by radiologists.
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