Quantification of Cardiac Dyssynchrony Entropy Implemented in a Commercial Nuclear Medicine Software Application

Baughan, Natalie; Poitrasson-Rivière, Alexis; Moody, Jonathan B.; Lee, Benjamin C.; Ficaro, Edward P.

doi:10.1080/00295450.2019.1708142

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Furthermore, GSPECT MPI is considerably being used for HF patients to identify LV dyssynchrony, LVEF, LV volumes, ischemia, viability, and scar tissue. Moreover, the advantages include the state of being ubiquitous and automated [ 19 , 23 , 49 ].…”

Section: Discussionmentioning

confidence: 99%

“…The study by Rastgou et al [ 48 ] stated that the phase analysis parameters of Emory Cardiac Toolbox and QGS program are well correlated, but these parameters should not be used interchangeably. Furthermore, in relation to entropy (a parameter in phase analysis), the lower value of this parameter indicates synchronized heart contraction whereas the higher value indicates desynchronize heart contraction [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

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Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition

Sabouri

Hajianfar²,

Hosseini³

et al. 2022

J Digit Imaging

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A U-shaped contraction pattern was shown to be associated with a better Cardiac resynchronization therapy (CRT) response. The main goal of this study is to automatically recognize left ventricular contractile patterns using machine learning algorithms trained on conventional quantitative features (ConQuaFea) and radiomic features extracted from Gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). Among 98 patients with standard resting GSPECT MPI included in this study, 29 received CRT therapy and 69 did not (also had CRT inclusion criteria but did not receive treatment yet at the time of data collection, or refused treatment). A total of 69 non-CRT patients were employed for training, and the 29 were employed for testing. The models were built utilizing features from three distinct feature sets (ConQuaFea, radiomics, and ConQuaFea + radiomics (combined)), which were chosen using Recursive feature elimination (RFE) feature selection (FS), and then trained using seven different machine learning (ML) classifiers. In addition, CRT outcome prediction was assessed by different treatment inclusion criteria as the study’s final phase. The MLP classifier had the highest performance among ConQuaFea models (AUC, SEN, SPE = 0.80, 0.85, 0.76). RF achieved the best performance in terms of AUC, SEN, and SPE with values of 0.65, 0.62, and 0.68, respectively, among radiomic models. GB and RF approaches achieved the best AUC, SEN, and SPE values of 0.78, 0.92, and 0.63 and 0.74, 0.93, and 0.56, respectively, among the combined models. A promising outcome was obtained when using radiomic and ConQuaFea from GSPECT MPI to detect left ventricular contractile patterns by machine learning.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition

Sabouri

Hajianfar²,

Hosseini³

et al. 2022

J Digit Imaging

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show abstract

“…This, in turn, resulted in largely pragmatic strategies to provide emergency support and guidance as practitioners adapted their assessment diets to the demands of hybrid models of learning and teaching. The high-risk nature of many traditional summative assessment methods such as the unseen, time-constrained, examinations was exposed as a feature of normative assessment patterns and practices, further strengthening concerns around the inclusivity of alternative approaches being deployed (Baughan et al, 2020). Assessment in higher education serves several purposes.…”

Section: A Shifting Assessment Landscapementioning

confidence: 99%

The (dis)continuities of digital transformation in flexible assessment practice: Educator perspectives on what matters most

Elkington

Chesterton

2023

Studies in Technology Enhanced Learning

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Quantification of Cardiac Dyssynchrony Entropy Implemented in a Commercial Nuclear Medicine Software Application

Cited by 2 publications

References 17 publications

Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition

Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition

The (dis)continuities of digital transformation in flexible assessment practice: Educator perspectives on what matters most

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