Reliability and feasibility of automated function imaging for quantification in patients with left ventricular dilation: comparison with cardiac magnetic resonance

Chen, Yefen; Hua, Wei; Yang, Wenbo; Shi, Zhicai; Fang, Yuehui

doi:10.1007/s10554-021-02510-x

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Cardiac MRI has high accuracy and reproducibility in assessing left ventricular function, morphology, volume, and mass. [6][7][8][9] The longitudinal myocardial function can also be evaluated by cardiac MRI through detection of the mitral annular displacement, which is a potentially sensitive parameter of the left ventricular systolic function. [10][11][12][13] Moreover, cardiac MRI is free from radiation and noninvasive, allows imaging acquisition in any desired plane, and can provide images with excellent temporal and spatial resolution irrespective of patient features or operator's experience especially for 3-Tesla MRI, therefore being currently considered the reference standard in evaluating cardiac function.…”

Section: Introductionmentioning

confidence: 99%

Three-Tesla magnetic resonance imaging of left ventricular volume and function in comparison with computed tomography and echocardiography

Guo

Liu

et al. 2023

Medicine

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This study investigated the correlation between 3-Tesla magnetic resonance imaging (MRI) and 256 multiple-slice computed tomography (MSCT) or 2-dimensional echocardiography (ECHO) in evaluating left ventricle. Forty patients were retrospectively enrolled to undergo cardiac MSCT, 3-Tesla MRI and 2-dimensional ECHO within 1 week. The end-diastolic (EDV) and end-systolic volume (ESV), stroke volume (SV) and ejection fraction (EF) were analyzed and compared. MSCT was highly significantly correlated with MRI. Compared with MRI, MSCT slightly overestimated ESV for about 8.7 mL, but slightly underestimated EF and SV for about 6.8% and 5.8 mL, respectively. A high consistency existed between MSCT and MRI, with the 95% limit of agreement (−19.6, 25.4) mL for EDV, (−2.6,20.1) mL for ESV, (−28.3,16.6) mL for SV, and (−18.8%,5.1) % for EF. ECHO was also significantly correlated with MRI. The ECHO slightly underestimated the left ventricular function compared with MRI, with an underestimation of 9.4 mL for EDV, 3.5 mL for ESV, 5.8 mL for SV and 1.0% for EF. A wider agreement limit existed between MRI and ECHO. MSCT has a better correlation and agreement relationship with MRI parameters than 2-dimensional ECHO in assessing the left ventricle and may serve as a possible alternative to MRI.

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Section: Introductionmentioning

confidence: 99%

Three-Tesla magnetic resonance imaging of left ventricular volume and function in comparison with computed tomography and echocardiography

Guo

Liu

et al. 2023

Medicine

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“…However, in the presence of significant knowledge gaps the technology may fall short of this potential. Presently, semi-automated assessments are in clinical use and accepted as a standard, feasible method for LV strain assessment, supported by evidence from numerous studies have supported the use of these methods ( 13 – 16 ). However, the human-software interaction is such that the current semi-automated approach yields values that are highly influenced by the level of experience and training of the sonographer.…”

Section: Discussionmentioning

confidence: 98%

Clinical service evaluation of the feasibility and reproducibility of novel artificial intelligence based-echocardiographic quantification of global longitudinal strain and left ventricular ejection fraction in trastuzumab-treated patients

Jiang,

Liu,

et al. 2023

Front. Cardiovasc. Med.

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IntroductionCardiotoxicity is a potential prognostically important complication of certain chemotherapeutic agents that may result in preclinical or overt clinical heart failure. In some cases, chemotherapy must be withheld when left ventricular (LV) systolic function becomes significantly impaired, to protect cardiac function at the expense of a change in the oncological treatment plan, leading to associated changes in oncological prognosis. Accordingly, patients receiving potentially cardiotoxic chemotherapy undergo routine surveillance before, during and following completion of therapy, usually with transthoracic echocardiography (TTE). Recent advancements in AI-based cardiac imaging reveal areas of promise but key challenges remain. There are ongoing questions as to whether the ability of AI to detect subtle changes in individual patients is at a level equivalent to manual analysis. This raises the question as to whether AI-based left ventricular strain analysis could provide a potential solution to left ventricular systolic function analysis in a manner equivocal to or superior to conventional assessment, in a real-world clinical service. AI based automated analyses may represent a potential solution for addressing the pressure of increasing echocardiographic demands within limited service-capacity healthcare systems, in addition to facilitating more accurate diagnoses.MethodsThis clinical service evaluation aims to establish whether AI-automated analysis compared to conventional methods (1) is a feasible method for assessing LV-GLS and LVEF, (2) yields moderate to good correlation between the two approaches, and (3) would lead to different clinical recommendations with serial surveillance in a real-world clinical population.Results and DiscussionWe observed a moderate correlation (r = 0.541) in GLS between AI automated assessment compared to conventional methods. The LVEF quantification between methods demonstrated a strong correlation (r = 0.895). AI-generated GLS and LVEF values compared reasonably well with conventional methods, demonstrating a similar temporal pattern throughout echocardiographic surveillance. The apical-three chamber view demonstrated the lowest correlation (r = 0.423) and revealed to be least successful for acquisition of GLS and LVEF. Compared to conventional methodology, AI-automated analysis has a significantly lower feasibility rate, demonstrating a success rate of 14% (GLS) and 51% (LVEF).

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“…The determinants affecting the left ventricular remodeling: (I) the intensity, longevity, and quickness of elevation in pressure burden; (II) the volume load; (III) factors like age, racial/ethnic background, and gender; (IV) accompanying conditions including coronary artery disease, diabetes, obesity, and valve-related heart issues; (V) the hormonal environment within the nervous system; (VI) changes in the extracellular matrix; and (VII) hereditary influences [ 6 ]. Left ventricular enlargement (LVE), as a common manifestation of ventricular remodeling, is closely associated with symptoms such as cardiac dysfunction, HF, and arrhythmias [ 7 ]. The increase in left ventricular size due to LVE exerts a severe impact on the clinical prognosis of patients with mild with or without severe HF [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients

Wu,

Ono,

Wang

et al. 2024

BioMed Eng OnLine

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Background Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals. Method We constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients. Results The classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients. Conclusion The proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling.

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Reliability and feasibility of automated function imaging for quantification in patients with left ventricular dilation: comparison with cardiac magnetic resonance

Cited by 4 publications

References 26 publications

Three-Tesla magnetic resonance imaging of left ventricular volume and function in comparison with computed tomography and echocardiography

Three-Tesla magnetic resonance imaging of left ventricular volume and function in comparison with computed tomography and echocardiography

Clinical service evaluation of the feasibility and reproducibility of novel artificial intelligence based-echocardiographic quantification of global longitudinal strain and left ventricular ejection fraction in trastuzumab-treated patients

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients

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