Yahyatabar Mohammad, Jouvet Philippe, Fily Donatien, Rambaud Jérome, Levy Michaël, Khemani Robinder G, Cheriet Farida
Department of Computer and Software Engineering, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada.
Department of Pediatrics, Faculty of Medicine, University of Montréal, Montréal, QC H3C 3J7, Canada.
Diagnostics (Basel). 2023 Mar 1;13(5):933. doi: 10.3390/diagnostics13050933.
Acute respiratory distress syndrome (ARDS), including severe pulmonary COVID infection, is associated with a high mortality rate. It is crucial to detect ARDS early, as a late diagnosis may lead to serious complications in treatment. One of the challenges in ARDS diagnosis is chest X-ray (CXR) interpretation. ARDS causes diffuse infiltrates through the lungs that must be identified using chest radiography. In this paper, we present a web-based platform leveraging artificial intelligence (AI) to automatically assess pediatric ARDS (PARDS) using CXR images. Our system computes a severity score to identify and grade ARDS in CXR images. Moreover, the platform provides an image highlighting the lung fields, which can be utilized for prospective AI-based systems. A deep learning (DL) approach is employed to analyze the input data. A novel DL model, named Dense-Ynet, is trained using a CXR dataset in which clinical specialists previously labelled the two halves (upper and lower) of each lung. The assessment results show that our platform achieves a recall rate of 95.25% and a precision of 88.02%. The web platform, named PARDS-CxR, assigns severity scores to input CXR images that are compatible with current definitions of ARDS and PARDS. Once it has undergone external validation, PARDS-CxR will serve as an essential component in a clinical AI framework for diagnosing ARDS.
急性呼吸窘迫综合征（ARDS），包括严重的肺部新冠感染，与高死亡率相关。早期检测ARDS至关重要，因为晚期诊断可能导致治疗中出现严重并发症。ARDS诊断的挑战之一是胸部X光（CXR）解读。ARDS会导致肺部出现弥漫性浸润，必须通过胸部X光检查来识别。在本文中，我们展示了一个基于网络的平台，该平台利用人工智能（AI）通过CXR图像自动评估小儿急性呼吸窘迫综合征（PARDS）。我们的系统计算严重程度评分，以识别和分级CXR图像中的ARDS。此外，该平台提供突出显示肺野的图像，可用于基于AI的前瞻性系统。采用深度学习（DL）方法分析输入数据。一种名为Dense-Ynet的新型DL模型使用一个CXR数据集进行训练，在该数据集中，临床专家先前已对每个肺的上下两半进行了标注。评估结果表明，我们的平台召回率达到95.25%，精确率达到88.02%。这个名为PARDS-CxR的网络平台为输入的CXR图像分配与ARDS和PARDS当前定义相符的严重程度评分。一旦经过外部验证，PARDS-CxR将成为临床AI诊断ARDS框架的重要组成部分。
