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 Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Training Dataset ◽  
Independent Dataset ◽  
X Ray ◽  
Learning Classifier ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

scholarly journals Atypical Chest X-Ray Calcification in an Idiopathic Constrictive Pericarditis Case

2013 ◽  
Vol 2013 ◽  
pp. 1-4
Author(s):  
Uğur Coşkun ◽  
İsmail Polat Canbolat ◽  
Ümit Yaşar Sinan ◽  
Cem Bostan ◽  
Kadriye Kılıçkesmez ◽  
...  
Keyword(s):  
Constrictive Pericarditis ◽  
Systolic Function ◽  
Cardiac Computed Tomography ◽  
Left Ventricular ◽  
Functional Class ◽  
Diastolic Filling ◽  
Heart Catheterization ◽  
X Ray ◽  
Atrioventricular Groove ◽  
Chest X Ray

Constrictive pericarditis is an uncommon cause of heart failure. It is a clinical entity caused by thickening, fibrosis, and/or calcification of the pericardium. We present a 50-year-old female patient who was admitted to our institution with a 6-month history of progressive dyspnea on exertion, abdominal swelling, and lower extremity edema. Her chest X-ray revealed an oblique linear calcification in the cardiac silhouette. Transthoracic echocardiography revealed biatrial enlargement. Left ventricular size and systolic function were normal. Cardiac computed tomography revealed the pericardial thickening (>5 mm) and heavy calcification in left atrioventricular groove. Simultaneous right and left heart catheterization showed elevation and equalization of right-sided and left-sided diastolic filling pressures, with characteristic dip, and plateau. Pericardiectomy was performed which revealed a thick, fibrous, calcified, and densely adherent pericardium constricting the heart. The postoperative period was uneventful and was in NYHA functional class I after 3 months.

scholarly journals Strange case of biventricular heart failure

2021 ◽  
Vol 14 (1) ◽  
pp. e239658
Author(s):  
Carloalberto Biolè ◽  
Matteo Bianco ◽  
Antonella Parente ◽  
Laura Montagna
Keyword(s):  
Heart Failure ◽  
Pulmonary Embolism ◽  
Cardiac Amyloidosis ◽  
Left Ventricular ◽  
Pulmonary Congestion ◽  
Transthyretin Amyloidosis ◽  
X Ray ◽  
Infiltrative Cardiomyopathy ◽  
Mild Reduction ◽  
Chest X Ray

Acute heart failure (HF) is commonly caused by a cardiomyopathy with one or more precipitating factor. Here, a case in which a cardiomyopathy is precipitated by pulmonary embolism (PE). A 77-year-old man is admitted for breathlessness and leg swelling. A mild reduction of left ventricular (LV) ejection fraction is found, with moderately increased LV wall thickness and pulmonary hypertension; clinical examination revealed signs of congestion with bilateral leg swelling, and mild signs of left HF with the absence of pulmonary congestion on chest X-ray. The ECG showed Mobitz I second-degree atrioventricular block. The clinical scenario led us to the diagnosis of infiltrative cardiomyopathy due to cardiac amyloidosis (CA) precipitated by PE. Pulmonary embolism is an overlooked precipitant of HF and can be the first manifestation of an underlying misdiagnosed cardiomyopathy, especially CA. 3,3-Diphosphono-1,2-propanodicarboxylic acid scan is a cornerstone in the diagnosis of Transthyretin amyloidosis (ATTR) cardiac amyloidosis.

Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography

2020 ◽  
Vol 77 (9) ◽  
pp. 597-602
Author(s):  
Xiaohua Wang ◽  
Juezhao Yu ◽  
Qiao Zhu ◽  
Shuqiang Li ◽  
Zanmei Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Classification Performance ◽  
X Ray ◽  
Testing Dataset ◽  
Learning Techniques ◽  
History Of ◽  
Digital Chest ◽  
Chest X Ray

ObjectivesTo investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.MethodsWe retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.ResultsThe Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, p<0.001).ConclusionOur experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.

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 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

scholarly journals Prognostic value of the chest X-ray in patients hospitalised for heart failure

2021 ◽  
Author(s):  
Daniel Pan ◽  
Pierpaolo Pellicori ◽  
Karen Dobbs ◽  
Jeanne Bulemfu ◽  
Ioanna Sokoreli ◽  
...  
Keyword(s):  
Heart Failure ◽  
Prognostic Value ◽  
Left Ventricular ◽  
Venous Congestion ◽  
Pleural Effusions ◽  
X Ray ◽  
Cardiothoracic Ratio ◽  
Chest X Ray ◽  
Alveolar Oedema ◽  
B Lines

Abstract Background Patients admitted to hospital with heart failure will have had a chest X-ray (CXR), but little is known about their prognostic significance. We aimed to report the prevalence and prognostic value of the initial chest radiograph findings in patients admitted to hospital with heart failure (acute heart failure, AHF). Methods The erect CXRs of all patients admitted with AHF between October 2012 and November 2016 were reviewed for pulmonary venous congestion, Kerley B lines, pleural effusions and alveolar oedema. Film projection (whether anterior–posterior [AP] or posterior–anterior [PA]) and cardiothoracic ratio (CTR) were also recorded. Trial registration: ISRCTN96643197 Results Of 1145 patients enrolled, 975 [median (interquartile range) age 77 (68–83) years, 61% with moderate, or worse, left ventricular systolic dysfunction, and median NT-proBNP 5047 (2337–10,945) ng/l] had an adequate initial radiograph, of which 691 (71%) were AP. The median CTR was 0.57 (IQR 0.53–0.61) in PA films and 0.60 (0.55–0.64) in AP films. Pulmonary venous congestion was present in 756 (78%) of films, Kerley B lines in 688 (71%), pleural effusions in 649 (67%) and alveolar oedema in 622 (64%). A CXR score was constructed using the above features. Increasing score was associated with increasing age, urea, NT-proBNP, and decreasing systolic blood pressure, haemoglobin and albumin; and with all-cause mortality on multivariable analysis (hazard ratio 1.10, 95% confidence intervals 1.07–1.13, p < 0.001). Conclusions Radiographic evidence of congestion on a CXR is very common in patients with AHF and is associated with other clinical measures of worse prognosis. Graphic abstract

Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure using Standard Chest X-Ray

2021 ◽  
Author(s):  
Yukina Hirata ◽  
Kenya Kusunose ◽  
Takumasa Tsuji ◽  
Kohei Fujimori ◽  
Jun’ichi Kotoku ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pulmonary Artery ◽  
Pulmonary Artery Wedge Pressure ◽  
X Ray ◽  
Chest X Ray ◽  
Wedge Pressure

scholarly journals A Novel Convolutional Neural Network for COVID-19 detection and classification using Chest X-Ray images

2021 ◽  
Author(s):  
Muhammad Talha Nafees ◽  
Irshad ullah ◽  
Muhammad Rizwan ◽  
Maaz ullah ◽  
Muhammad Irfanullah Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Rapid Diagnosis ◽  
Training Dataset ◽  
Validation Dataset ◽  
Medical Professionals ◽  
X Ray ◽  
Expert Radiologist ◽  
Chest X Ray

The early and rapid diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), the main cause of fatal pandemic coronavirus disease 2019 (COVID-19), with the analysis of patients chest X-ray (CXR) images has lifesaving importance for both patients and medical professionals. In this research a very simple novel and robust deep-learning convolutional neural network (CNN) model with less number of trainable-parameters is proposed to assist the radiologists and physicians in the early detection of COVID-19 patients. It also helps to classify patients into COVID-19, pneumonia and normal on the bases of analysis of augmented X-ray images. This augmented dataset contains 4803 COVID-19 from 686 publicly available chest X-ray images along with 5000 normal and 5000 pneumonia samples. These images are divided into 80% training and 20 % validation. The proposed CNN model is trained on training dataset and then tested on validation dataset. This model has a promising performance with a mean accuracy of 92.29%, precision of 99.96%, Specificity of 99.85% along with Sensitivity value of 85.92%. The result can further be improved if more data of expert radiologist is publically available.

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