Effects of Mesenchymal Stem Cell Injection into Healed Myocardial Infarction Scar Border Zone on the Risk of Ventricular Tachycardia

Park, Eun-Hye; Kim, Jin-Moo; Seong, EunHwa; Lee, Eunmi; Chang, Kiyuk; Choi, Young

doi:10.3390/biomedicines11082141

Cited by 1 publication

(1 citation statement)

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“…IMS remains pertinent to current research with new studies endeavoring to find new treatments 17 and improve identification, 18 underscoring the ongoing research interest in advancing our understanding of IMS. At the same time, CMR imaging has sustained its relevance in cardiovascular research, with the prevalence of recent studies demonstrating continued research interest in advancing the diagnostic capabilities and roles of CMR imaging for heart conditions.…”

Section: Related Workmentioning

confidence: 99%

Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification

Udin,

Armstrong,

Kai

et al. 2024

J. Med. Imag.

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Ischemic myocardial scarring (IMS) is a common outcome of coronary artery disease that potentially leads to lethal arrythmias and heart failure. Late-gadolinium-enhanced cardiac magnetic resonance (CMR) imaging scans have served as the diagnostic bedrock for IMS, with recent advancements in machine learning enabling enhanced scar classification. However, the trade-off for these improvements is intensive computational and time demands. As a solution, we propose a combination of lightweight preprocessing (LWP) and template matching (TM) to streamline IMS classification.Approach: CMR images from 279 patients (151 IMS, 128 control) were classified for IMS presence using two convolutional neural networks (CNNs) and TM, both with and without LWP. Evaluation metrics included accuracy, sensitivity, specificity, F1score, area under the receiver operating characteristic curve (AUROC), and processing time. External testing dataset analysis encompassed patient-level classifications (PLCs) and a CNN versus TM classification comparison (CVTCC).Results: LWP enhanced the speed of both CNNs (4.9x) and TM (21.9x). Furthermore, in the absence of LWP, TM outpaced CNNs by over 10x, while with LWP, TM was more than 100x faster. Additionally, TM performed similarly to the CNNs in accuracy, sensitivity, specificity, F1-score, and AUROC, with PLCs demonstrating improvements across all five metrics. Moreover, the CVTCC revealed a substantial 90.9% agreement.Conclusions: Our results highlight the effectiveness of LWP and TM in streamlining IMS classification. Anticipated enhancements to LWP's region of interest (ROI) isolation and TM's ROI targeting are expected to boost accuracy, positioning them as a potential alternative to CNNs for IMS classification, supporting the need for further research.

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Section: Related Workmentioning

confidence: 99%