The 5-Year Prognosis in Patients With Mi Type 4a Suffering From Stable Coronary Artery Disease and Arterial Hypertension

Ushanova, A. M.; Mironova, Olga Iu.

doi:10.1097/01.hjh.0000572368.55146.54

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“…Studies suggested that cardiac MRI is substantially more sensitive than conventional cardiac ultrasound in the diagnosis of diseases, such as myocardial injury. In addition, it is not only suitable for the diagnosis of suspected myocarditis but also for the etiological diagnosis of patients with similar acute coronary syndrome but normal coronary angiography [ 13 , 14 ]. Convolutional neural network (CNN) is feedforward, which effectively reduces the complexity of the feedback network.…”

Section: Introductionmentioning

confidence: 99%

Protection of Ivabradine Combined with Trimetazidine on Myocardial Injury after Percutaneous Coronary Intervention in Patients with Coronary Artery Disease Evaluated by Magnetic Resonance Image under Convolutional Neural Network

Chen¹

2021

Contrast Media & Molecular Imaging

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Objective. To evaluate the myocardial protection of Ivabradine (IBD) combined with Trimetazidine (TMZ) in patients with coronary artery disease (CAD) after percutaneous coronary intervention (PCI), magnetic resonance imaging (MRI) images under convolutional neural network (CNN) algorithm were used. Methods. A CNN artificial intelligence algorithm was proposed to process the image artifacts caused by undersampling magnetic resonance, so as to be used in the diagnosis and efficacy evaluation of myocardial injury. 120 patients with CAD after PCI were rolled into group A (TMZ treatment), group B (IBD treatment), and group C (IBD + TMZ combined treatment) randomly, with 40 patients in each group. All the patients were treated for two consecutive weeks and followed up for six months. Clinical indicators of patients in the two groups were observed, detected, and statistically analyzed. Results. The accuracy, sensitivity, specificity, and area under the curve (AUC) of MRI images based on CNN algorithm for the diagnosis of myocardial injury were 91.04%, 97.60%, 87.04%, and 96.43%, respectively. After treatment, the left ventricular end diastolic diameter (LVEDD), LVE diastolic volume (LVEDV), LVE systolic diameter (LVESD), and LVE systolic volume (LVESV) were greatly reduced in all groups after treatment, whereas the left ventricular ejection fraction (LVEF) increased considerably ( P < 0.05 ). LVEDD, LVEDV, LVESD, and LVESV in group C were substantially inferior to those in groups A and B, and LVEF was remarkably superior to that in groups A and B ( P < 0.05 ). After treatment, cTnI, hs-CRP, sICAM-1, ET-1, and MDA in three groups were greatly decreased ( P < 0.05 ), while SOD was substantially increased ( P < 0.05 ). After treatment, cTnI, hs-CRP, SICAM-1, ET-1, and MDA in group C were notably inferior to groups A and B ( P < 0.05 ), while SOD was greatly higher ( P < 0.05 ). Conclusion. MRI based on CNN had high application value in the diagnosis and efficacy evaluation of myocardial injury after PCI. For patients with CAD, IBD combined with TMZ after PCI can effectively play the role of anti-inflammatory and antioxidative damage and improve intradermal function.

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Section: Introductionmentioning

confidence: 99%

Protection of Ivabradine Combined with Trimetazidine on Myocardial Injury after Percutaneous Coronary Intervention in Patients with Coronary Artery Disease Evaluated by Magnetic Resonance Image under Convolutional Neural Network

Chen¹

2021

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

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The 5-Year Prognosis in Patients With Mi Type 4a Suffering From Stable Coronary Artery Disease and Arterial Hypertension

Cited by 1 publication

References 0 publications

Protection of Ivabradine Combined with Trimetazidine on Myocardial Injury after Percutaneous Coronary Intervention in Patients with Coronary Artery Disease Evaluated by Magnetic Resonance Image under Convolutional Neural Network

Protection of Ivabradine Combined with Trimetazidine on Myocardial Injury after Percutaneous Coronary Intervention in Patients with Coronary Artery Disease Evaluated by Magnetic Resonance Image under Convolutional Neural Network

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