Journal of electrocardiology最新文献

{"title":"Can electrocardiographic repolarization predict diabetes incidence: the Atherosclerosis Risk in Communities Study","authors":"Chunyu Zhang ,&nbsp;Peng Yu ,&nbsp;Ming Liu ,&nbsp;Lei Zhang ,&nbsp;Xiaomu Li ,&nbsp;Hong Jiang","doi":"10.1016/j.jelectrocard.2025.153885","DOIUrl":"10.1016/j.jelectrocard.2025.153885","url":null,"abstract":"<div><h3>Background</h3><div>Diabetes is marked by metabolic dysregulation and high cardiovascular risk. Preceding diabetes onset, cardiac change markers often appear, yet predicting occurrence of diabetes remains challenging.</div></div><div><h3>Methods</h3><div>The study included 11,297 ARIC Study participants aged 45–64 without baseline diabetes or heart disease, with 12‑lead ECGs recorded. Cox regression models were used to analyze repolarization parameters (QTc intervals, J-point level, T-wave amplitude, T-wave to R-wave (T/R) ratio) in relation to diabetes risk. Correlation analyses explored links between ECG findings, metabolic parameters, and myocardial fat content.</div></div><div><h3>Results</h3><div>Over 23 years, 3338 participants (29.5 %) developed type 2 diabetes. A reduced T/R ratio showed dose-response relationship with diabetes risk, notably in lead I (HR 1.22, 95 % CI 1.15–1.31) and lead V5 (HR 1.15, 95 % CI 1.07–1.24) per standard deviation (SD) decrease after adjustment for common covariates. J-point level and T-wave amplitude also exhibited associations, though weaker. Negative correlations were found between repolarization markers, including T-wave amplitude, T/R ratio with metabolic parameters.</div></div><div><h3>Conclusions</h3><div>ECG parameters, especially T-wave amplitude and T/R ratio, predict incident type 2 diabetes and serve as potential early biomarkers for metabolic-induced cardiac change. These findings underscore their clinical relevance in identifying individuals at risk for diabetes.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153885"},"PeriodicalIF":1.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review and meta-analysis on the performance of convolutional neural networks ECGs in the diagnosis of hypertrophic cardiomyopathy","authors":"Ivo Queiroz ,&nbsp;Maria L.R. Defante ,&nbsp;Lucas M. Barbosa ,&nbsp;Arthur Henrique Tavares ,&nbsp;Túlio Pimentel ,&nbsp;Beatriz Ximenes Mendes","doi":"10.1016/j.jelectrocard.2025.153888","DOIUrl":"10.1016/j.jelectrocard.2025.153888","url":null,"abstract":"<div><h3>Introduction</h3><div>Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death in younger individuals. Accurate diagnosis is crucial for management and improving patient outcomes. The application of convolutional Neural Networks (CNN), a type of AI modeling, to electrocardiogram (ECG) analysis, presents a promising and optimistic avenue for the detection of HCM. We conducted a meta-analysis to assess the effectiveness of CNN models in diagnosing HCM through ECG.</div></div><div><h3>Methods</h3><div>MEDLINE, Embase, and Cochrane were searched up to August 12, 2024, focusing on CNN ECG-based HCM detection models. The outcomes were sensitivity, specificity, and SROC. Pooled proportions were calculated using a random-effects model with 95 % confidence intervals (CIs), and heterogeneity was assessed using the I² statistics. This study was registered on PROSPERO protocol CRD42024581925.</div></div><div><h3>Results</h3><div>Our analysis included 16 studies with ECG data from 513,972 patients. The AI algorithms employed CNNs for ECG interpretation. Sixteen studies contributed to the qualitative analysis, while seven studies for the pooled SROC with an 11 % false positive rate, with a sensitivity of 89 % (95 % CI 86–92 %) and a specificity of 88 % (95 % CI 81–93 %).</div></div><div><h3>Conclusion</h3><div>AI-driven ECG interpretation shows high accuracy and sensitivity in detecting HCM, though the modest PPV suggests that AI should be integrated with clinical evaluation to enhance reliability, particularly in screening settings.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153888"},"PeriodicalIF":1.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143329057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation between ECG and MCE findings in HCM patients and clinical implications","authors":"Baihetiya Tayier ,&nbsp;Chao Yuan ,&nbsp;Liyun Liu ,&nbsp;Lina Guan ,&nbsp;Yuming Mu","doi":"10.1016/j.jelectrocard.2025.153886","DOIUrl":"10.1016/j.jelectrocard.2025.153886","url":null,"abstract":"<div><h3>Background</h3><div>ST segment depression (STD) on electrocardiogram (ECG) is the most frequently used examination to detect myocardial ischemia and myocardial contrast echocardiography (MCE) is considered as a reliable technique to assess myocardial purfusion. However, association between two examinations and the underlying clinical implication in hypertrophic cardiomyopathy (HCM) patients is not fully illustrated.</div></div><div><h3>Objective</h3><div>To investigate the correlation between ECG and MCE findings in HCM patients and elucidating the underlying clinical implications.</div></div><div><h3>Methods</h3><div>Thirty-two patients diagnosed with HCM comprising the HCM cohort and 28 healthy individuals were enrolled as controls. The amplitude of ST segment depression (STD) was assessed, and the following MCE parameters: peak intensity (PI), area under the curve (AUC), rising slope (RS) and time to peak (TTP) were recorded and compared between the two groups, and correlation between MCE parameters and the extent of STD was calculated. Furthermore, HCM patients were categorized into three subgroups according to the severity of STD: ST1 group (0 &lt; STD ≤ 0.1 mV); ST2 group (0.1 mV &lt; STD ≤ 0.2 mV); ST3 group (0.2 mV &lt; STD ≤ 0.3 mV), and data was compared among the four groups.</div></div><div><h3>Results</h3><div>ECG showed that all patients in the HCM group present STD (<em>t</em> = 8.294, <em>P</em> &lt; 0.001). MCE showed that the values of PI, RS, and AUC were significantly reduced in the HCM group as compared to the control group (<em>P</em> &lt; 0.001). Results of correlation analysis show no linear correlation between the PI values and the extent of STD (<em>r</em> = −0.348, <em>P</em> = 0.051). Of note, a significant difference in PI values between ST1 and ST3 (<em>P</em> = 0.01), ST2 and ST3 (<em>P</em> = 0.023) was observed.</div></div><div><h3>Conclusion</h3><div>Our findings reveal that STD greater than 0.2 mV strongly indicates myocardial perfusion impairment in HCM patients, and can serve as a reliable index for stratifying patients and identifying those at high risk.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153886"},"PeriodicalIF":1.3,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can artificial intelligence lower the global sudden cardiac death rate? A narrative review","authors":"Raja Savanth Reddy Chityala ,&nbsp;Sandhya Bishwakarma ,&nbsp;Kaival Malav Shah ,&nbsp;Ashmita Pandey ,&nbsp;Muhammad Saad","doi":"10.1016/j.jelectrocard.2025.153882","DOIUrl":"10.1016/j.jelectrocard.2025.153882","url":null,"abstract":"<div><h3>Purpose of review</h3><div>WHO defines SCD as sudden unexpected death either within 1 h of symptom onset (witnessed) or within 24 h of having been observed alive and symptom-free (unwitnessed). Sudden cardiac arrest is a major cause of mortality worldwide, with survival to hospital discharge for hospital cardiac arrest and in-hospital cardiac arrest being only 9.3 % and 21.2 %, respectively, despite treatment highlighting the importance of effectively predicting and preventing cardiac arrest. This literature review aims to explore the role and application of AI (Artificial Intelligence) in predicting and preventing sudden cardiac arrest.</div></div><div><h3>Material and methods</h3><div>Eligible studies were searched from PubMed and Web of Science. The inclusion criteria were fulfilled if sudden cardiac death prediction and prevention, artificial intelligence, machine learning, and deep learning were included.</div></div><div><h3>Conclusions</h3><div>Artificial intelligence, machine learning, and deep learning have shown remarkable prospects in SCA risk stratification, which can improve the survival rate from SCA. Nonetheless, they have not been adequately trained and tested, necessitating further studies with explainable techniques, larger sample sizes, external validation, more diverse patient samples, multimodal tools, ethics, and bias mitigation to unlock their full potential.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153882"},"PeriodicalIF":1.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to correct QT interval after cardiac resynchronisation therapy","authors":"Amin Esmailian ,&nbsp;Colin Machado ,&nbsp;Hui Chen Han ,&nbsp;Jeffrey Alison ,&nbsp;Mohammad Alasti","doi":"10.1016/j.jelectrocard.2025.153881","DOIUrl":"10.1016/j.jelectrocard.2025.153881","url":null,"abstract":"<div><h3>Introduction</h3><div>This study evaluates various formulae used to correct the QT interval in patients with wide QRS complexes to calculate corrected QT (QTc) following Cardiac Resynchronisation Therapy (CRT).</div></div><div><h3>Methods</h3><div>We included patients with severe heart failure and left bundle branch block, presenting with a QRS duration of at least 120 milliseconds, who underwent successful CRT implantation. Patients were excluded if they had non-lateral left ventricular lead placement, metabolic disorders, atrial fibrillation, atrial tachycardia, or high-degree atrioventricular block prior to implantation. QT intervals were measured pre- and post-implantation and corrected for QRS duration and heart rate using the Boggosian, Wand, Rautaharju, Yankelson, Tang &amp; Rabkin, Bazett, Framingham, Hodges and Fredericia formulae.</div></div><div><h3>Results</h3><div>A total of 51 patients met the study criteria. After CRT, the QRS duration significantly decreased from 189.68 ± 18.06 milliseconds to 165.25 ± 18.78 milliseconds. However, the QT interval corrected using Bazett's formula showed no significant change (522.30 ± 33.37 milliseconds versus 524.06 ± 36.52 milliseconds). Among the various correction methods, the combination of the Bogossian formula (for QRS duration) followed by the Hodges formula (for heart rate), or the Rautaharju formula followed by the Fredericia formula, produced comparable QT intervals. Similarly, correcting heart rate with the Fredericia formula followed by QRS correction with the Rautaharju formula yielded consistent results.</div></div><div><h3>Conclusion</h3><div>Our findings indicate that different formulae for correcting QT intervals for heart rate and QRS duration may yield varying results. Notably, the use of the Bogossian formula followed by the Hodges formula, or the combination of the Rautaharju and Fredericia formulae, produces relatively consistent QT intervals before and after CRT. Further research is needed to validate these findings.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153881"},"PeriodicalIF":1.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-dimensional standard electrocardiogram: A first approach based on precordial leads","authors":"Alejandro Jesús Bermejo Valdés","doi":"10.1016/j.jelectrocard.2025.153875","DOIUrl":"10.1016/j.jelectrocard.2025.153875","url":null,"abstract":"<div><h3>Background</h3><div>Traditional electrocardiography (ECG) is limited to two-dimensional (2D) representations, which restricts its ability to capture the full complexity of cardiac electrical activity. We propose a novel three-dimensional (3D) ECG methodology directly derived from standard 2D recordings, providing enhanced spatial insights without requiring additional hardware modifications. Additionally, we introduce a new formulation, which we term “almost-curvature”, to optimize the detection of variations in acute ischemic states, which is one of the leading causes of mortality and exhibits sensitivity issues in diagnosis using traditional ECG methods.</div></div><div><h3>Objective</h3><div>To introduce the methodology for developing 3D ECG and evaluate its diagnostic utility in detecting morphological changes associated with acute myocardial ischemia through perimeter, curvature, and almost-curvature metrics.</div></div><div><h3>Methods</h3><div>We developed a methodology based on spherical-to-Cartesian coordinate transformations applied to standard ECG data from the PhysioNet database. For validation, we utilized datasets from patients with induced myocardial ischemia. We extracted perimeter, curvature, and almost-curvature metrics from both 3D and 2D ECG and compared them across different ischemic states. From method implementation to clinical validation, we used clustering analyses and statistical tests such as Anderson-Darling, Kolmogorov-Smirnov, Mantel, Shapiro-Wilk, Wilcoxon, and the Permutation test.</div></div><div><h3>Results</h3><div>Statistical analysis revealed a correlation between the plane presenting standard deflections in the 3D ECG and the plane displaying the novel loops generated by our methodology. The almost-curvature metric demonstrated an enhanced capacity to detect variations between ischemic states, surpassing the diagnostic performance of traditional curvature metrics. While 2D analyses showed a reduction in curvature and perimeter during ischemia progression, 3D ECG analysis revealed an increase in these metrics, underscoring its ability to capture morphological changes that may be overlooked by conventional methods.</div></div><div><h3>Conclusion</h3><div>3D ECG analysis shows potential to enhance the detection of ischemic alterations, offering a more detailed spatial representation of cardiac electrical activity compared to traditional methods. By leveraging spherical-to-Cartesian transformations, our methodology integrates temporal and voltage dynamics into a unified framework, potentially revealing subtle morphological changes associated with ischemia. Although the methodology remains in its early developmental stage and requires further refinement, its promising diagnostic utility suggests it could significantly enhance cardiac diagnostics. Further studies are essential to validate its clinical applicability and address current limitations.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153875"},"PeriodicalIF":1.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mathematical model for derivation of Elevated-Electrode-Placement Electrocardiogram (EEP-ECG) leads from a standard 12-lead electrocardiogram","authors":"Karan Kalani MD DM,&nbsp;Raja Selvaraj MD DNB,&nbsp;Sreekumaran Nair PhD FSMS,&nbsp;Santhosh Satheesh MD DM,&nbsp;Avinash Anantharaj MD DM,&nbsp;Shaheer Ahmed MD DM,&nbsp;Suresh Kumar Sukumaran MD DM,&nbsp;Anish Bhargav MD DM,&nbsp;Sridhar Balaguru MD DM","doi":"10.1016/j.jelectrocard.2025.153880","DOIUrl":"10.1016/j.jelectrocard.2025.153880","url":null,"abstract":"<div><h3>Background</h3><div>Placement of right precordial leads in higher intercostal spaces (EEP-ECG) improves the detection of Brugada Syndrome (BrS). Given the potential difficulty of lead placement and the transient nature of BrS ECG patterns, we developed a model to predict EEP-ECG from a standard 12‑lead ECG.</div></div><div><h3>Objective</h3><div>To create and validate a model that derives EEP-ECG leads from a standard 12‑lead ECG.</div></div><div><h3>Methods</h3><div>We recorded 16 channel ECGs (12 standard leads plus 4 elevated leads in the 2nd and 3rd intercostal spaces) using two identical ECG recorders. A linear regression model was developed from the ECG data in the training group. This model was subsequently used to predict EEP-ECG in the validation group. Accuracy of the model was evaluated by comparing the derived leads to the actually recorded leads. Comparison was done using correlation coefficients and visual assessment by two cardiologists on a scale of 1–3.</div></div><div><h3>Results</h3><div>The study included 42 participants (22 in the training group, 20 in the validation group), including 8 BrS patients. The model showed strong correlation (<em>r</em> &gt; 0.85) between actual and predicted leads for 76 of 80 leads. Visual assessment yielded an average score of 2.44 ± 0.68. The model has been made available as an online tool for automatic derivation of EEP-ECG from a standard 12‑lead ECG (<span><span>http://eep-ecg.in/</span><svg><path></path></svg></span>).</div></div><div><h3>Conclusion</h3><div>We developed a linear model to derive elevated ECG leads from standard 12‑lead ECGs. The model predicts EEP-ECG with reasonable accuracy. This model can be useful in diagnosing BrS in new or existing ECGs.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153880"},"PeriodicalIF":1.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spontaneous resumption of severe infranodal conduction disturbances that followed COVID-19 vaccination","authors":"Takashi Nakashima MD, PhD ,&nbsp;Takahiro Usui MD ,&nbsp;Mikihito Morimoto MD ,&nbsp;Masaru Nagase MD ,&nbsp;Kei Ando MD ,&nbsp;Taro Shibahara MD ,&nbsp;Daiju Ono MD ,&nbsp;Takehiro Yamada MD ,&nbsp;Keita Suzuki MD ,&nbsp;Makoto Yamaura MD ,&nbsp;Takahisa Ido MD ,&nbsp;Shigekiyo Takahashi MD, PhD ,&nbsp;Takuma Aoyama MD, PhD","doi":"10.1016/j.jelectrocard.2025.153874","DOIUrl":"10.1016/j.jelectrocard.2025.153874","url":null,"abstract":"<div><div>We report reversible severe infranodal conduction disturbances that followed COVID-19 vaccination in a young woman. Right and left bundle branch conduction were impaired and recovered at different times, resulting in reversible paroxysmal complete atrioventricular block.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153874"},"PeriodicalIF":1.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating very low ejection fraction from the 12 Lead ECG among patients with acute heart failure","authors":"Sunita Pokhrel Bhattarai PhD, RN ,&nbsp;Dillon J. Dzikowicz PhD, RN, PCCN ,&nbsp;Ying Xue DNSc, RN ,&nbsp;Robert Block MD, MPH, FACP, FNLA ,&nbsp;Rebecca G. Tucker PhD, RN, ACNPC ,&nbsp;Shilpa Bhandari BCS ,&nbsp;Victoria E. Boulware BSN, RN ,&nbsp;Breanne Stone BSN, RN ,&nbsp;Mary G. Carey PhD RN, FAHA FAAN","doi":"10.1016/j.jelectrocard.2025.153878","DOIUrl":"10.1016/j.jelectrocard.2025.153878","url":null,"abstract":"<div><h3>Background</h3><div>Identifying patients with low left ventricular ejection fraction (LVEF) in the emergency department using an electrocardiogram (ECG) may optimize acute heart failure (AHF) management. We aimed to assess the efficacy of 527 automated 12‑lead ECG features for estimating LVEF among patients with AHF.</div></div><div><h3>Method</h3><div>Medical records of patients &gt;18 years old and AHF-related ICD codes, demographics, LVEF %, comorbidities, and medication were analyzed. Least Absolute Shrinkage and Selection Operator (LASSO) identified important ECG features and evaluated performance.</div></div><div><h3>Results</h3><div>Among 851 patients, the mean age was 74 years (IQR:11), male 56 % (<em>n</em> = 478), and the median body mass index was 29 kg/m² (IQR:1.8). A total of 914 echocardiograms and ECGs were matched; the time between ECG-Echocardiogram was 9 h (IQR of 9 h); ≤30 % LVEF (16.45 %, <em>n</em> = 140). Lasso demonstrated 42 ECG features important for estimating LVEF ≤30 %. The predictive model of LVEF ≤30 % showed an area under the curve (AUC) of 0.86, a 95 % confidence interval (CI) of 0.83 to 0.89, a specificity of 54 % (50 % to 57 %), and a sensitivity of 91 (95 % CI: 88 % to 96 %), accuracy 60 % (95 % CI:60 % to 63 %) and, negative predictive value of 95 %.</div></div><div><h3>Conclusions</h3><div>An explainable machine learning model with physiologically feasible predictors may help screen patients with low LVEF in AHF.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153878"},"PeriodicalIF":1.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The crucial role of image quality in AI-enabled ECG digitization and interpretation of occlusion myocardial infarction","authors":"Robert Herman ,&nbsp;Stephen W. Smith","doi":"10.1016/j.jelectrocard.2025.153873","DOIUrl":"10.1016/j.jelectrocard.2025.153873","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153873"},"PeriodicalIF":1.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}