scholarly journals Erratum: Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning

2020 ◽  
Vol 61 (5) ◽  
pp. 1088-1088
Author(s):  
Takuya Matsumoto ◽  
Satoshi Kodera ◽  
Hiroki Shinohara ◽  
Hirotaka Ieki ◽  
Toshihiro Yamaguchi ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
X Ray ◽  
Chest X Ray

Abstract 13157: Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-ray

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Yukina Hirata ◽  
Kenya Kusunose ◽  
Hirotsugu Yamada ◽  
Takumasa Tsuji ◽  
Kohei Fujimori ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Objective Evaluation ◽  
Left Ventricular ◽  
Training Dataset ◽  
Heart Catheterization ◽  
X Ray ◽  
Chest X Ray ◽  
Wedge Pressure

Introduction: Chest X-ray (CXR) is a useful and economical modality for the detection of congestive heart failure. However, the accuracy is limited by the subjective nature of its interpretation. Deep learning (DL) can be used to recognize diseases or findings objectively in various imaging modalities, and may outperform previous diagnostic techniques. Hypothesis: We hypothesized that DL-based analysis of CXR detect the presence of elevated pulmonary arterial wedge pressure (PAWP) in patients with suspected heart failure. Methods: We enrolled 1,013 patients with paired right heart catheterization and CXR performed from October 2009 to February 2020 in our hospital. DL algorithm for the detection of elevated PAWP was developed using the training dataset, based on a single CXR image. Independent evaluation cohort of 115 patients was performed using CXR-based DL model and echocardiographic data to detect the presence of high PAWP. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the DL-based models compared with echocardiographic data. Results: The study included 1,013 patients (mean age, 67±13 years; 569 males [56%]). The mean PAWP was 12.5±6.4 mmHg and 218 patients (22%) had more than 18mmHg. To detect high PAWP, the AUC produced by DL algorithms was effective, and the DL algorithm with the largest AUC was ResNet50. In an evaluation cohort, to detect high PAWP, the AUC using the DL model with CXR was similar to the AUC produced by the echocardiographic left ventricular diastolic dysfunction algorithm (0.77 vs. 0.70; respectively; p=0.27), and significantly higher than the AUC by measurements of echocardiographic parameters (ResNet50 vs. other parameters; all compared p <0.05) (Figure) . Conclusions: The present results demonstrated that DL based on analysis of CXR can detect the presence of high PAWP. This finding suggests that the DL based approach may support an objective evaluation of CXR in the clinical setting.

scholarly journals Diagnosing heart failure from chest X-ray images using deep learning

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Matsumoto ◽  
S Kodera ◽  
H Shinohara ◽  
A Kiyosue ◽  
Y Higashikuni ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
Funding Source ◽  
X Rays ◽  
X Ray ◽  
Cardiothoracic Ratio ◽  
Prediction Probability ◽  
Chest X Ray

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 and cardiomegaly in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this study, 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 respectively verified and relabeled these images, for a total of 260 “normal” and 378 “heart failure” images, and the remainder were discarded because they had been incorrectly labeled. In this study “heart failure” was defined as “cardiomegaly or congestion”, in a chest X-ray with cardiothoracic ratio (CTR) over 50% or radiographic presence of pulmonary edema. To enable the machine to extract a sufficient number of features from the images, we used the general machine learning approach called data augmentation and transfer learning. Owing mostly to this technique and the adequate relabeling process, we established a model to detect heart failure in chest X-ray by applying deep learning, and obtained an accuracy of 82%. Sensitivity and specificity to heart failure were 75% and 94.4%, respectively. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. The figure shows randomly selected examples of the prediction probabilities and heatmaps of the chest X-rays from the dataset. The original image is on the left and its heatmap is on the right, with its prediction probability written below. The red areas on the heatmaps show important regions, according to which the machine determined the classification. While some images with ambiguous radiolucency such as (e) and (f) were prone to be misdiagnosed by this model, most of the images like (a)–(d) were diagnosed correctly. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images. Heatmaps and probabilities of prediction Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI

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

2020 ◽  
Vol 61 (4) ◽  
pp. 781-786
Author(s):  
Takuya Matsumoto ◽  
Satoshi Kodera ◽  
Hiroki Shinohara ◽  
Hirotaka Ieki ◽  
Toshihiro Yamaguchi ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
X Ray ◽  
Chest X Ray

scholarly journals Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19

2021 ◽  
Author(s):  
Abdullahi Umar Ibrahim ◽  
Mehmet Ozsoz ◽  
Sertan Serte ◽  
Fadi Al-Turjman ◽  
Polycarp Shizawaliyi Yakoi
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Chest X Ray

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

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