Distribution Characteristics of ST-Segment Elevation Myocardial Infarction and Non-ST-Segment Elevation Myocardial Infarction Culprit Lesion in Acute Myocardial Infarction Patients Based on Coronary Angiography Diagnosis

Abstract

This research was aimed at exploring the application value of coronary angiography (CAG) based on a convolutional neural network algorithm in analyzing the distribution characteristics of ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) culprit lesions in acute myocardial infarction (AMI) patients. Methods. Patients with AMI treated in hospital from June 2019 to December 2020 were selected as subjects. According to the results of an echocardiogram, the patients were divided into the STEMI group (44 cases) and the NSTEMI group (36 cases). All patients received CAG. All images were denoised and edge detected by a convolutional neural network algorithm. Then, the number of diseased vessels, the location of diseased vessels, and the degree of stenosis of diseased vessels in the two groups were compared and analyzed. Results. The number of patients with complete occlusion (3 cases vs. 12 cases) and collateral circulation (5 cases vs. 20 cases) in the NSTEMI group was significantly higher than that in the STEMI group, and the difference was statistically significant, P < 0.05 . There was a statistically significant difference in the number of lesions between the distal LAD (1 case vs. 10 cases) and the distal LCX (4 cases vs. 11 cases), P < 0.05 . There was a statistically significant difference in the number of patients with one lesion branch (1 vs. 18) and three lesion branches (25 vs. 12) between the two groups, P < 0.05 . The image quality after the convolution neural network algorithm is significantly improved, and the lesion is more prominent. Conclusion. The convolutional neural network algorithm has good performance in DSA image processing of AMI patients. STEMI and NSTEMI as the starting point of AMI disease analysis to determine the treatment plan have high clinical application value. This work provided reference and basis for the application of the convolutional neural network algorithm and CAG in the analysis of the distribution characteristics of STEMI and NSTEMI culprit lesions in AMI patients.

The Frequency of Common Deafness-Associated Variants Among 3,555,336 Newborns in China and 141,456 Individuals Across Seven Populations Worldwide

“…In addition, the most frequent variant was GJB2 c.235delC, and its frequency was extraordinarily high compared with those of other variants detected in this study, with an allele frequency of 0.99%. When compared with other Asian populations, this frequency was similar to those found among the general populations in the Korea (0.500%–0.744%) (Park et al 2000, 2003; Bae et al 2008; Han et al 2008; Sagong et al 2013) and Japan (0.426%–0.786%) (Kudo et al 2000; Tsukada et al 2010; Taniguchi et al 2015; Maeda et al 2020) and previously studied Chinese populations (0.638%–1.229%) (Table 3) (Wu et al 2011; Zhang et al 2012; Chen et al 2015; Peng et al 2016; Wu et al 2017; Hao et al 2018; He et al 2018; Dai et al 2019; Zou et al 2019; Cao et al 2020, 2022; Cai et al 2021). GJB2 encodes the gap junction protein connexin 26 (Cx26), which is expressed in the nonsensory cells of the cochlea.…”

Retracted: Distribution Characteristics of ST‐Segment Elevation Myocardial Infarction and Non‐ST‐Segment Elevation Myocardial Infarction Culprit Lesion in Acute Myocardial Infarction Patients Based on Coronary Angiography Diagnosis

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”