Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning

Abstract

The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this paper, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists verified and relabeled a total of 260 "normal" and 378 "heart failure" images, with the remainder being discarded because they had been incorrectly labeled. Data augmentation and transfer learning were used to obtain an accuracy of 82% in diagnosing heart failure using the chest X-ray images. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images.

Quantitative estimation of pulmonary artery wedge pressure from chest radiographs by a regression convolutional neural network

“…CNN has become an effective method for detecting and classifying various diseases [8][9][10]. In the field of cardiovascular medicine, recent studies found that CNN was useful for detecting cardiomegaly, heart failure, and elevated PAWP from chest radiographs [11][12][13]. However, it is not able to quantitatively estimate PAWP.…”

X-Rays and Computed Tomography Scan Imaging: Instrumentation and Medical Applications

“…Technology advances of electronics made use of X-ray for digital imaging by replacing the traditional X-ray-sensitive film by electronic sensors [7][8][9][10]. Today, both convention and digital X-ray imaging modalities are the prompt and main diagnostic tools for investigating and screening the chest for viral and bacterial pneumonia, tuberculosis, lung cancer [11][12][13][14][15][16][17][18][19], enlarged heart, and blocked blood vessels [20][21][22][23][24]; the bones and teeth for fractures and infections, arthritis, bone cancer, and dental decay [25][26][27][28][29][30]; the abdomen for digestive tract problems and looking for swallowed items [31]. Moreover, other modalities for Xray imaging have been developed such as digital mammography for breast cancer screening [32].…”

Deep learning-based approach for detecting signs of atrial septal defect on chest radiographs: a proof of concept study

“…Recent advances in artificial intelligence (AI) technology are dramatically transforming healthcare and medicine [8, 9]. Especially, image recognition by using deep learning is increasingly applied to identification, classification, and quantification of patterns in medical images [10], such as X-ray radiography [11, 12], echocardiography [13], computed tomography [14], and magnetic resonance imaging [15]. Deep learning is a subset of machine learning that employs multilayered neural networks to learn representations of the given input data with multiple levels of abstraction and provide the final output without help from humans [16, 17].…”

“…perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted February 1, 2022. ; https://doi.org/10.1101/2022.01.30.22270137 doi: medRxiv preprint of patterns in medical images [10], such as X-ray radiography [11,12], echocardiography [13], computed tomography [14], and magnetic resonance imaging [15]. Deep learning is a subset of machine learning that employs multilayered neural networks to learn representations of the given input data with multiple levels of abstraction and provide the final output without help from humans [16,17].…”