A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation

2021 ◽  
Author(s):  
Cherry Kim ◽  
Gaeun Lee ◽  
Hongmin Oh ◽  
Gyujun Jeong ◽  
Sun Won Kim ◽  
...  
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Valvular Heart Disease ◽  
External Validation ◽  
Chest Radiographs ◽  
Automatic Analysis ◽  
Disease Development

scholarly journals Prediction of Pulmonary to Systemic Flow Ratio in Patients With Congenital Heart Disease Using Deep Learning–Based Analysis of Chest Radiographs

2020 ◽  
Vol 5 (4) ◽  
pp. 449 ◽  
Author(s):  
Shuhei Toba ◽  
Yoshihide Mitani ◽  
Noriko Yodoya ◽  
Hiroyuki Ohashi ◽  
Hirofumi Sawada ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Deep Learning ◽  
Congenital Heart ◽  
Chest Radiographs ◽  
Flow Ratio

scholarly journals A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform

2020 ◽  
Author(s):  
John S. Chorba ◽  
Avi M. Shapiro ◽  
Le Le ◽  
John Maidens ◽  
John Prince ◽  
...  
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Mitral Regurgitation ◽  
Aortic Stenosis ◽  
Valvular Heart Disease ◽  
Learning Algorithm ◽  
Cardiac Murmur ◽  
Significant Information ◽  
Deep Learning Algorithm ◽  
Clinically Significant

AbstractBackgroundThere is variability among clinicians in their ability to detect murmurs during cardiac auscultation and identify the underlying pathology. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information.ObjectiveThe objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform.MethodsUsing over 34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 373 patients in a clinical study and collected recordings at the four primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by three expert cardiologists.ResultsAlgorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1, sensitivity increases to 90.0%. The algorithm detects moderate-to-severe or greater aortic stenosis with sensitivity of 97.5% and specificity of 77.7% and detects moderate-to-severe or greater mitral regurgitation with sensitivity of 64.0% and specificity of 90.5%.ConclusionThe deep learning algorithm’s ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists. The research findings attest to the reliability and utility of such algorithms as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease.

scholarly journals Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs

2020 ◽  
pp. 2003061
Author(s):  
Ju Gang Nam ◽  
Minchul Kim ◽  
Jongchan Park ◽  
Eui Jin Hwang ◽  
Jong Hyuk Lee ◽  
...  
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Learning Algorithm ◽  
External Validation ◽  
Chest Radiographs ◽  
Reading Tests ◽  
Deep Learning Algorithm ◽  
The Mean ◽  
Emergency Department Patients ◽  
Development And Validation

We aimed to develop a deep-learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs and to evaluate its impact in diagnostic accuracy, timeliness of reporting, and workflow efficacy.DLAD-10 was trained with 146 717 radiographs from 108 053 patients using a ResNet34-based neural network with lesion-specific channels for 10 common radiologic abnormalities (pneumothorax, mediastinal widening, pneumoperitoneum, nodule/mass, consolidation, pleural effusion, linear atelectasis, fibrosis, calcification, and cardiomegaly). For external validation, the performance of DLAD-10 on a same-day CT-confirmed dataset (normal:abnormal, 53:147) and an open-source dataset (PadChest; normal:abnormal, 339:334) was compared to that of three radiologists. Separate simulated reading tests were conducted on another dataset adjusted to real-world disease prevalence in the emergency department, consisting of four critical, 52 urgent, and 146 non-urgent cases. Six radiologists participated in the simulated reading sessions with and without DLAD-10.DLAD-10 exhibited areas under the receiver-operating characteristic curves (AUROCs) of 0.895–1.00 in the CT-confirmed dataset and 0.913–0.997 in the PadChest dataset. DLAD-10 correctly classified significantly more critical abnormalities (95.0% [57/60]) than pooled radiologists (84.4% [152/180]; p=0.01). In simulated reading tests for emergency department patients, pooled readers detected significantly more critical (70.8% [17/24] versus 29.2% [7/24]; p=0.006) and urgent (82.7% [258/312] versus 78.2% [244/312]; p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean time-to-report critical and urgent radiographs (640.5±466.3 versus 3371.0±1352.5 s and 1840.3±1141.1 versus 2127.1±1468.2, respectively; p-values<0.01) and reduced the mean interpretation time (20.5±22.8 versus 23.5±23.7 s; p<0.001).DLAD-10 showed excellent performance, improving radiologists' performance and shortening the reporting time for critical and urgent cases.

scholarly journals Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform

2021 ◽  
Author(s):  
John S. Chorba ◽  
Avi M. Shapiro ◽  
Le Le ◽  
John Maidens ◽  
John Prince ◽  
...  
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Mitral Regurgitation ◽  
Aortic Stenosis ◽  
Valvular Heart Disease ◽  
Learning Algorithm ◽  
Cardiac Murmur ◽  
Significant Information ◽  
Deep Learning Algorithm ◽  
Clinically Significant

Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate‐to‐severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate‐to‐severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm’s ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front‐line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov ; Unique Identifier: NCT03458806.

scholarly journals Deep Learning Systems for Pneumothorax Detection on Chest Radiographs: A Multicenter External Validation Study

2021 ◽  
pp. e200190
Author(s):  
Yee Liang Thian ◽  
Dian Wen Ng ◽  
James Thomas Patrick Decourcy Hallinan ◽  
Pooja Jagmohan ◽  
David Soon Yiew Sia ◽  
...  
Keyword(s):  
Deep Learning ◽  
Validation Study ◽  
External Validation ◽  
Learning Systems ◽  
Chest Radiographs ◽  
External Validation Study
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