Yahyatabar Mohammad, Jouvet Philippe, Cheriet Farida
Polytechnique Montréal, Department of Computer and Software Engineering, Montreal, Quebec, Canada.
University of Montréal, Department of Pediatrics, Faculty of Medicine, Montréal, Quebec, Canada.
J Med Imaging (Bellingham). 2023 Sep;10(5):054504. doi: 10.1117/1.JMI.10.5.054504. Epub 2023 Oct 17.
Acute respiratory distress syndrome (ARDS) is a life-threatening condition that can cause a dramatic drop in blood oxygen levels due to widespread lung inflammation. Chest radiography is widely used as a primary modality to detect ARDS due to its crucial role in diagnosing the syndrome, and the x-ray images can be obtained promptly. However, despite the extensive literature on chest x-ray (CXR) image analysis, there is limited research on ARDS diagnosis due to the scarcity of ARDS-labeled datasets. Additionally, many machine learning-based approaches result in high performance in pulmonary disease diagnosis, but their decisions are often not easily interpretable, which can hinder their clinical acceptance. This work aims to develop a method for detecting signs of ARDS in CXR images that can be clinically interpretable.
To achieve this goal, an ARDS-labeled dataset of chest radiography images is gathered and annotated for training and evaluation of the proposed approach. The proposed deep classification-segmentation model, Dense-Ynet, provides an interpretable framework for automatically diagnosing ARDS in CXR images. The model takes advantage of lung segmentation in diagnosing ARDS. By definition, ARDS causes bilateral diffuse infiltrates throughout the lungs. To consider the local involvement of lung areas, each lung is divided into upper and lower halves, and our model classifies the resulting lung quadrants.
The quadrant-based classification strategy yields the area under the receiver operating characteristic curve of 95.1% (95% CI 93.5 to 96.1), which allows for providing a reference for the model's predictions. In terms of segmentation, the model accurately identifies lung regions in CXR images even when lung boundaries are unclear in abnormal images.
This study provides an interpretable decision system for diagnosing ARDS, by following the definition used by clinicians for the diagnosis of ARDS from CXR images.
急性呼吸窘迫综合征（ARDS）是一种危及生命的疾病，由于广泛的肺部炎症可导致血氧水平急剧下降。胸部X线摄影因其在诊断该综合征中的关键作用而被广泛用作检测ARDS的主要手段，并且可以迅速获得X线图像。然而，尽管关于胸部X线（CXR）图像分析的文献很多，但由于ARDS标记数据集的稀缺，关于ARDS诊断的研究仍然有限。此外，许多基于机器学习的方法在肺部疾病诊断中具有较高的性能，但其决策往往不易解释，这可能会阻碍它们在临床上的应用。这项工作旨在开发一种在CXR图像中检测ARDS迹象的方法，该方法在临床上是可解释的。
为实现这一目标，收集并标注了一个ARDS标记的胸部X线摄影图像数据集，用于所提出方法的训练和评估。所提出的深度分类-分割模型Dense-Ynet为在CXR图像中自动诊断ARDS提供了一个可解释的框架。该模型在诊断ARDS时利用了肺部分割。根据定义，ARDS会导致双侧肺部弥漫性浸润。为了考虑肺部区域的局部受累情况，将每个肺部分为上半部分和下半部分，然后我们的模型对得到的肺象限进行分类。
基于象限的分类策略在受试者操作特征曲线下的面积为95.（95%置信区间93.5至96.1），这为模型的预测提供了参考。在分割方面，即使在异常图像中肺边界不清楚时，该模型也能准确识别CXR图像中的肺区域。
本研究通过遵循临床医生从CXR图像诊断ARDS所使用的定义，提供了一个用于诊断ARDS的可解释决策系统。
