Detecting Heart Failure from Chest X-Ray Images Using Deep Learning Algorithms

Omary, Sara El; Lahrache, Souad; Ouazzani, Rajae El

doi:10.1109/menacomm50742.2021.9678291

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The DenseNet-121 24 architecture was used as the backbone of the model as it has been shown to learn effective representations of CXRs using a series of convolutions and residual connections. 19 , 20 , 25 The representation of the image at the last layer of the neural network along with sex and the continuous age of the patient at the time of the CXR was combined to produce a single data vector. Using this vector, the model produced estimates for the three continuous echocardiogram measurements: LVPWd, IVSd, and LVIDd.…”

Section: Methodsmentioning

confidence: 99%

Deep learning to detect left ventricular structural abnormalities in chest X-rays

Bhave,

Rodriguez,

Poterucha

et al. 2024

European Heart Journal

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Background and Aims Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. Methods A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. Results The model yielded an AUROC of 0.79 (0.76–0.81) for SLVH, 0.80 (0.77–0.84) for DLV, and 0.80 (0.78–0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. Conclusions Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.

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Section: Methodsmentioning

confidence: 99%

Deep learning to detect left ventricular structural abnormalities in chest X-rays

Bhave,

Rodriguez,

Poterucha

et al. 2024

European Heart Journal

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“…Recently, machine and DL studies are widely investigated, as they have shown sophisticated results in many problems such as CVDs. For example, El Omary et al [8] employed serveral CNNs for the purpose of detecting cardiac arrhythmia based on electrocardiogram (ECG) two-dimensional (2D) images; in addition, they [9] utilized a variety of pre-trained CNN models to diagnose heart failure in Radiograph images. Next, Yang et al [10] introduced a model aiming at early heart failure diagnosis using a combination of Bayesian principal component analysis (BPCA) and support vector machine (SVM) resulting in an accuracy rate of 74.4%.…”

Section: Related Workmentioning

confidence: 99%

Attention mechanism-based model for cardiomegaly recognition in chest X-Ray images

El Omary,

Lahrache,

El Ouazzani

2024

IJ-AI

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Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions.Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.

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