P4347Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
F M Asch ◽  
N Poilvert ◽  
T Abraham ◽  
M Jankowski ◽  
J Cleve ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ejection Fraction ◽  
Reference Values ◽  
Learning Algorithm ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Machine Learning Algorithm ◽  
Computer Algorithms ◽  
Human Expert ◽  
Lv Volumes

Abstract Background Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by standard calculation of model-based end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could be developed, which circumvents border detection and instead estimates the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images. Purpose This study was designed to test the feasibility and accuracy of this approach. Methods Machine learning algorithm was developed and trained on a database of >50,000 echocardiographic studies, including multiple apical 2- and 4-chamber views, to automatically estimate LVEF (AutoEF, BayLabs). Testing was performed on an independent group of 99 unselected patients, whose automated EF values were compared to reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis of bias and limits of agreement (LOA). Consistency was assessed by mean absolute deviation (MAD) among automated estimates based on different combinations of apical views. Finally, sensitivity and specificity of detecting of EF≤35% was calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers. Results Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (MAD=2.9%) and excellent agreement with the reference values: r=0.95, bias=1.0%, LOA=±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF≤35%. This was similar to clinicians' measurements: r=0.94, bias=1.4%, LOA=±13.4%,sensitivity 0.93, specificity 0.87. Conclusions Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared to reference values provided by an expert panel. Acknowledgement/Funding Bay Labs, Inc.

scholarly journals Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert

2019 ◽  
Vol 12 (9) ◽  
Author(s):  
Federico M. Asch ◽  
Nicolas Poilvert ◽  
Theodore Abraham ◽  
Madeline Jankowski ◽  
Jayne Cleve ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ejection Fraction ◽  
Reference Values ◽  
Learning Algorithm ◽  
Left Ventricular ◽  
Machine Learning Algorithm ◽  
Mean Absolute Deviation ◽  
Limits Of Agreement ◽  
Absolute Deviation ◽  
Lv Volumes

Background: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could circumvent border detection and instead would estimate the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images. Methods: Machine learning algorithm was developed and trained to automatically estimate LVEF on a database of >50 000 echocardiographic studies, including multiple apical 2- and 4-chamber views (AutoEF, BayLabs). Testing was performed on an independent group of 99 patients, whose automated EF values were compared with reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis. Consistency was assessed by mean absolute deviation among automated estimates from different combinations of apical views. Finally, sensitivity and specificity of detecting of EF ≤35% were calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers. Results: Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (mean absolute deviation =2.9%) and excellent agreement with the reference values: r =0.95, bias=1.0%, limits of agreement =±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF ≤35%. This was similar to clinicians’ measurements: r =0.94, bias=1.4%, limits of agreement =±13.4%, sensitivity 0.93, specificity 0.87. Conclusions: Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared with reference values provided by an expert panel.

scholarly journals Deep Learning–Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution

2021 ◽  
Author(s):  
Federico M. Asch ◽  
Victor Mor-Avi ◽  
David Rubenson ◽  
Steven Goldstein ◽  
Muhamed Saric ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ejection Fraction ◽  
Learning Algorithm ◽  
Point Of Care ◽  
Intraclass Correlation ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Lv Function ◽  
Machine Learning Algorithm ◽  
New Machine

Background: We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. Methods: Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%–73%), mildly-to-moderately (30%–52%), or severely reduced (<30%). Additionally, LV function was visually estimated for each view by 10 experienced physicians. Accuracy of the detection of reduced LV function (EF<53%) by the automated classification and physicians’ interpretation was assessed against the reference classification. In protocol 2, we tested the new machine-learning algorithm in the POC setting on images acquired by nurses using a portable imaging system. Results: Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86–0.95), with biases <2%. Machine-learning classification of LV function showed similar accuracy to that by physicians in most views, with only 10% to 15% cases where it was less accurate. Protocol 2: the agreement with the reference values was excellent (intraclass correlation=0.84) with a minimal bias of 2.5±6.4%. Conclusions: The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.

scholarly journals A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF

2020 ◽  
Author(s):  
Matthias Schneider ◽  
Philipp Bartko ◽  
Welf Geller ◽  
Varius Dannenberg ◽  
Andreas König ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical Students ◽  
Learning Algorithm ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Accurate Estimation ◽  
First Year ◽  
Machine Learning Algorithm ◽  
Diagnostic Quality ◽  
First Year Medical Students

Abstract Left ventricular ejection fraction (LVEF) is the most important parameter in the assessment of cardiac function. A machine-learning algorithm was trained to guide ultrasound-novices to acquire diagnostic echocardiography images. The artificial intelligence (AI) algorithm then estimates LVEF from the captured apical-4-chamber (AP4), apical-2-chamber (AP2), and parasternal-long-axis (PLAX) loops. We sought to test this algorithm by having first-year medical students without previous ultrasound knowledge scan real patients. Nineteen echo-naïve first-year medical students were trained in the basics of echocardiography by a 2.5 h online video tutorial. Each student then scanned three patients with the help of the AI. Image quality was graded according to the American College of Emergency Physicians scale. If rated as diagnostic quality, the AI calculated LVEF from the acquired loops (monoplane and also a “best-LVEF” considering all views acquired in the particular patient). These LVEF calculations were compared to images of the same patients captured and read by three experts (ground-truth LVEF [GT-EF]). The novices acquired diagnostic-quality images in 33/57 (58%), 49/57 (86%), and 39/57 (68%) patients in the PLAX, AP4, and AP2, respectively. At least one of the three views was obtained in 91% of the attempts. We found an excellent agreement between the machine’s LVEF calculations from images acquired by the novices with the GT-EF (bias of 3.5% ± 5.6 and r = 0.92, p < 0.001 in the “best-LVEF” algorithm). This pilot study shows first evidence that a machine-learning algorithm can guide ultrasound-novices to acquire diagnostic echo loops and provide an automated LVEF calculation that is in agreement with a human expert.

scholarly journals 153 Multiparametric assessment of diastolic function in heart failure

2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
T Uejima ◽  
J Cho ◽  
H Hayama ◽  
L Takahashi ◽  
J Yajima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Cluster Analysis ◽  
Diastolic Function ◽  
Primary Endpoint ◽  
Prognostic Value ◽  
Learning Algorithm ◽  
Left Ventricular ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm

Abstract Background The assessment of diastolic function is still challenging in the setting of heart failure (HF). We tested the hypothesis that applying a machine learning algorithm would detect heterogeneity in diastolic function and improve risk stratification in HF population. Methods This study included consecutive 279 patients with clinically stable HF referred for echocardiographic assessment, for whom diastolic function variables were measured according to the current guidelines. Cluster analysis, an unsupervised machine learning algorithm, was undertaken on these variables to form homogeneous groups of patients with similar profiles of the variables. Sequential Cox models paralleling the clinical sequence of HF assessment were used to elucidate the benefit of cluster-based classification over guidelines-based classification. The primary endpoint was a hospitalization for worsening HF. Results Cluster analysis identified 3 clusters with distinct properties of diastolic function that shared similarities with guidelines-based classification. The clusters were associated with brain natriuretic peptide level (p &lt; 0.001, figure A). During follow-up period of 2.6 ± 2.0 years, 62 patients (22%) experienced the primary endpoint. Cluster-based classification exhibited a significant prognostic value (c2 = 20.3, p &lt; 0.001, figure B), independent from and incremental to an established clinical risk score for HF (MAGGIC score) and left ventricular end-diastolic volume (hazard ratio = 1.677, p = 0.017, model c2: from 47.5 to 54.1, p = 0.015, figure D). Although guideline-based classification showed a significant prognostic value (c2 = 13.1, p = 0.001, figure C), it did not significantly improve overall prognostication from the baseline (model c2: from 47.5 to 49.9, p = 0.199, figure D). Conclusion Machine learning techniques help grading diastolic function and stratifying the risk for decompensation in HF. Abstract 153 Figure.

scholarly journals Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China

BMJ Open ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. e023724 ◽  
Author(s):  
Fanqi Meng ◽  
Zhihua Zhang ◽  
Xiaofeng Hou ◽  
Zhiyong Qian ◽  
Yao Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Sudden Cardiac Death ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Study Protocol ◽  
Cardiac Death ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction

IntroductionLeft ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF.Methods and analysisWe will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study.Ethics and disseminationThe study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences.Trial registration numberChiCTR-POC-17011842; Pre-results.

scholarly journals Innovative procedure for measuring left ventricular ejection fraction from 18F-FDG first-pass ultra-sensitive digital PET/CT images: evaluation with an anthropomorphic heart phantom

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Emilie Verrecchia-Ramos ◽  
Olivier Morel ◽  
Paul Retif ◽  
Sinan Ben Mahmoud
Keyword(s):  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Reference Values ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Pet Ct ◽  
First Pass ◽  
Digital Pet ◽  
3D Pet

Abstract Background Left ventricular ejection fraction (LVEF) is usually measured by cine-cardiac magnetic resonance imaging (MRI), planar and single-photon emission-computerized tomography (SPECT) equilibrium radionuclide angiocardiography (ERNA), and echocardiography. It would be clinically useful to measure LVEF from first-pass positron-emission tomography/computed tomography (PET/CT) radionuclide angiography, but this approach has been limited by fast radiotracer diffusion. Ultra-sensitive digital PET systems can produce high-quality images within 3-s acquisition times. This study determined whether digital PET/CT accurately measured LVEF in an anthropomorphic heart phantom under conditions mimicking radiotracer first-pass into the cardiac cavities. Methods Heart phantoms in end-diastole and end-systole were 3D-printed from a patient’s MRI dataset. Reference left ventricle end-diastole volume (EDV), end-systole volume (ESV), and LVEF were determined by phantom weights before/after water filling. PET/CT (3-s acquisitions), MRI, and planar and SPECT ERNA were performed. EDV, ESV, and/or LVEF were measured by manual and automated cardiac cavity delineation, using clinical segmentation softwares. LVEF was also measured from PET images converted to 2D “pseudo-planar” images along the short axis and horizontal long axis. LVEF was also calculated for planar ERNA images. All LVEF, ESV and EDV values were compared to the reference values assessed by weighing. Results Manually calculated 3D-PET-CT-based EDV, ESV, and LVEF were close to MRI and reference values. Automated calculations on the 3D-PET-CT dataset were unreliable, suggesting that the SPECT-based tool used for this calculation is not well adapted for PET acquisitions. Manual and automated LVEF estimations from “pseudo-planar” PET images were very close/identical to MRI and reference values. Conclusions First-pass “pseudo-planar” PET may be a promising method for estimating LVEF, easy to use in clinical practice. Processing 3D PET images is also a valid method but to date suffers from a lack of well-suited software for automated LV segmentation.

scholarly journals Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
M. Marcinkiewicz-Siemion ◽  
M. Kaminski ◽  
M. Ciborowski ◽  
K. Ptaszynska-Kopczynska ◽  
A. Szpakowicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Ejection Fraction ◽  
Current Knowledge ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Metabolic Derangement ◽  
Ventricular Ejection Fraction ◽  
Diagnostic Panel ◽  
Selection Of

AbstractThe metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for HFrEF. The study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatography-mass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of LPC 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in HFrEF. The accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. The combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers.

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