Myocardial perfusion artifacts in left bundle branch block: A diagnostic challenge

Gupta, Kartik; Bajaj, Navkaranbir S.; Hage, Fadi G.; Bhambhvani, Pradeep

doi:10.1007/s12350-019-01717-9

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Left bundle branch block (LBBB) is a frequent source of false positive reports in myocardial perfusion imaging (MPI). This has been reported in previous studies that have evaluated the relation between myocardial perfusion and LBBB [1,2].…”

Section: Introductionsupporting

confidence: 68%

Effectively Detecting Left Bundle Branch Block False Defects in Myocardial Perfusion Imaging (MPI) with a Convolutional Neural Network (CNN)

Abdi¹,

Naili²,

Habbache³

et al. 2022

Studies in Health Technology and Informatics

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Left bundle branch block (LBBB) is a frequent source of false positive MPI reports, in patients evaluated for coronary artery disease. Purpose: In this work, we evaluated the ability of a CNN-based solution, using transfer learning, to produce an expert-like judgment in recognizing LBBB false defects. Methods: We collected retrospectively, MPI polar maps, of patients having small to large fixed anteroseptal perfusion defect. Images were divided into two groups. The LBBB group included patients where this defect was judged as false defect by two experts. The LAD group included patients where this defect was judged as a true defect by two experts. We used a transfer learning approach on a CNN (ResNet50V2) to classify the images into two groups. Results: After 60 iterations, the reached accuracy plateau was 0.98, and the loss was 0.19 (the validation accuracy and loss were 0.91 and 0.25, respectively). A first test set of 23 images was used (11 LBBB, and 12 LAD). The empiric ROC (Receiver operating characteristic) Area was estimated at 0.98. A second test set (18x2 images) was collected after the final results. The ROC area was estimated again at 0.98. Conclusion: Artificial intelligence, using CNN and transfer learning, could reproduce an expert-like judgment in differentiating between LBBB false defects, and LAD real defects.

show abstract

Section: Introductionsupporting

confidence: 68%

Effectively Detecting Left Bundle Branch Block False Defects in Myocardial Perfusion Imaging (MPI) with a Convolutional Neural Network (CNN)

Abdi¹,

Naili²,

Habbache³

et al. 2022

Studies in Health Technology and Informatics

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show abstract

“…In an editorial by Gupta et al in this Journal in 2019 on myocardial perfusion artifacts in LBBB, 16 the following four research questions were raised that merited further investigation: The effort of Dr Vidula and colleagues is laudable but incomplete when taken into consideration the abovementioned research questions. Based on the present study I would add the following questions for future research studies:…”

Section: Introductionmentioning

confidence: 99%

Myocardial blood flow imaging in patients with a left bundle branch block or ventricular-paced rhythm: “And therein, as the Bard would tell us, lies the rub”

Danad¹,

Knaapen²

2021

Journal of Nuclear Cardiology

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“…Furthermore, it is well established that measures of LVAS are influenced by LV function, volume, scar burden and conduction abnormalities, [16][17][18] which also influence indices of myocardial perfusion. 19,20 The effect of these confounders on the relationship between LVAS and myocardial perfusion indices needs to be excluded to clearly elucidate the relationship between LVAS and subclinical flow heterogeneity. Additional clarity will be needed on the clinical application of LVAS for diagnosis of CAD-overt or subclinical.…”

mentioning

confidence: 99%

Left ventricular regional asynchrony: Earliest marker for ischemic cardiomyopathy?

Vij

Malhotra

2021

Journal of Nuclear Cardiology

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Myocardial perfusion artifacts in left bundle branch block: A diagnostic challenge

Abstract: Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.' Journal of Nuclear CardiologyÒ Gupta et al Myocardial perfusion artifacts in left bundle branch block

Cited by 6 publications

References 23 publications

Effectively Detecting Left Bundle Branch Block False Defects in Myocardial Perfusion Imaging (MPI) with a Convolutional Neural Network (CNN)

Effectively Detecting Left Bundle Branch Block False Defects in Myocardial Perfusion Imaging (MPI) with a Convolutional Neural Network (CNN)

Myocardial blood flow imaging in patients with a left bundle branch block or ventricular-paced rhythm: “And therein, as the Bard would tell us, lies the rub”

Left ventricular regional asynchrony: Earliest marker for ischemic cardiomyopathy?

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