Automatic assessment of left ventricular function for hemodynamic monitoring using artificial intelligence and transesophageal echocardiography

Yu, Jinyang; Taskén, Anders Austlid; Flade, Hans Martin; Skogvoll, Eirik; Berg, Erik Andreas Rye; Grenne, Bjørnar; Rimehaug, Audun; Kirkeby-Garstad, Idar; Kiss, Gabriel; Aakhus, Svend

doi:10.1007/s10877-023-01118-x

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…First, the patients in this study differ significantly from the patients in the training data [ 10 ]. Yet, autoMAPSE has demonstrated high feasibility, not only in this present study but also in cardiology patients [ 10 ] and in patients after isolated coronary bypass grafting [ 11 ]. Thus, autoMAPSE is likely generalizable to other critically ill patients.…”

Section: Discussionmentioning

confidence: 93%

“…We used autoMAPSE on all the recorded images, obtaining the MAPSE of each of the four LV walls automatically. AutoMAPSE is a custom-made software we have developed, validated [ 10 , 11 ], and refined [ 12 ] at our institution. The autoMAPSE pipeline comprises a convolutional neuronal network that was trained under supervised learning to detect the mitral annulus in TEE images [ 12 ], as well as a set of filtering algorithms for rejecting erroneous measurements (Fig.…”

Section: Methodsmentioning

confidence: 99%

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Continuous monitoring of left ventricular function in postoperative intensive care patients using artificial intelligence and transesophageal echocardiography

Yu,

Taskén,

Berg

et al. 2024

ICMx

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Background Continuous monitoring of mitral annular plane systolic excursion (MAPSE) using transesophageal echocardiography (TEE) may improve the evaluation of left ventricular (LV) function in postoperative intensive care patients. We aimed to assess the utility of continuous monitoring of LV function using TEE and artificial intelligence (autoMAPSE) in postoperative intensive care patients. Methods In this prospective observational study, we monitored 50 postoperative intensive care patients for 120 min immediately after cardiac surgery. We recorded a set of two-chamber and four-chamber TEE images every five minutes. We defined monitoring feasibility as how often the same wall from the same patient could be reassessed, and categorized monitoring feasibility as excellent if the same LV wall could be reassessed in ≥ 90% of the total recordings. To compare autoMAPSE with manual measurements, we rapidly recorded three sets of repeated images to assess precision (least significant change), bias, and limits of agreement (LOA). To assess the ability to identify changes (trending ability), we compared changes in autoMAPSE with the changes in manual measurements in images obtained during the initiation of cardiopulmonary bypass as well as before and after surgery. Results Monitoring feasibility was excellent in most patients (88%). Compared with manual measurements, autoMAPSE was more precise (least significant change 2.2 vs 3.1 mm, P < 0.001), had low bias (0.4 mm), and acceptable agreement (LOA − 2.7 to 3.5 mm). AutoMAPSE had excellent trending ability, as its measurements changed in the same direction as manual measurements (concordance rate 96%). Conclusion Continuous monitoring of LV function was feasible using autoMAPSE. Compared with manual measurements, autoMAPSE had excellent trending ability, low bias, acceptable agreement, and was more precise. Graphical Abstract

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Section: Discussionmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Continuous monitoring of left ventricular function in postoperative intensive care patients using artificial intelligence and transesophageal echocardiography

Yu,

Taskén,

Berg

et al. 2024

ICMx

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“…Yu and colleagues developed an algorithm for TEE to automatically calculate the mitral annular plane systolic excursion (MAPSE) which allows the evaluation of LV function. Using Bland-Altman analysis they found that autoMAPSE (AI generated MAPSE) compared to manual measurement had low bias (0.4 mm) and acceptable limits of agreement (−3.7 to 4.5 mm) [56].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction

Dadon,

Rav Acha,

Orlev

et al. 2024

Diagnostics

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Introduction: Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. Aim: To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. Methods: Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. Results: The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083–6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. Conclusion: AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.

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Cardiac Point-of-Care Ultrasound in Critical Care: Evolution, Current State, and Future Prospects

Diaz Milian,

Pandompatam,

Moreno Franco

2024

Curr Pulmonol Rep

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Automatic assessment of left ventricular function for hemodynamic monitoring using artificial intelligence and transesophageal echocardiography

Cited by 6 publications

References 35 publications

Continuous monitoring of left ventricular function in postoperative intensive care patients using artificial intelligence and transesophageal echocardiography

Continuous monitoring of left ventricular function in postoperative intensive care patients using artificial intelligence and transesophageal echocardiography

Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction

Cardiac Point-of-Care Ultrasound in Critical Care: Evolution, Current State, and Future Prospects

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