{"title":"Information for Readers","authors":"","doi":"10.1016/S0146-2806(25)00033-7","DOIUrl":"10.1016/S0146-2806(25)00033-7","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 3","pages":"Article 103010"},"PeriodicalIF":3.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Guidelines for Authors","authors":"","doi":"10.1016/S0146-2806(25)00038-6","DOIUrl":"10.1016/S0146-2806(25)00038-6","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 3","pages":"Article 103015"},"PeriodicalIF":3.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
André Luiz Carvalho Ferreira MD , Luanna Paula Garcez de Carvalho Feitoza , Maria E. Benitez MD , Buena Aziri MD , Edin Begic MD , Luciana Vergara Ferraz de Souza MD , Elísio Bulhões , Sarah O.N. Monteiro , Maria L.R. Defante , Roberto Augusto Mazetto Silva Vieira , Camila Guida MD
{"title":"Diagnostic accuracy of artificial-intelligence-based electrocardiogram algorithm to estimate heart failure with reduced ejection fraction: A systematic review and meta-analysis","authors":"André Luiz Carvalho Ferreira MD , Luanna Paula Garcez de Carvalho Feitoza , Maria E. Benitez MD , Buena Aziri MD , Edin Begic MD , Luciana Vergara Ferraz de Souza MD , Elísio Bulhões , Sarah O.N. Monteiro , Maria L.R. Defante , Roberto Augusto Mazetto Silva Vieira , Camila Guida MD","doi":"10.1016/j.cpcardiol.2025.103004","DOIUrl":"10.1016/j.cpcardiol.2025.103004","url":null,"abstract":"<div><h3>Introduction</h3><div>AI-based ECG has shown good accuracy in diagnosing heart failure. However, due to the heterogeneity of studies regarding cutoff points, its precision for specifically detecting heart failure with left ventricle reduced ejection fraction (LVEF <40 %) is not yet well established. What is the sensitivity and specificity of artificial-based electrocardiogram to diagnose heart failure with low ejection fraction (cut-off of 40 %. Aims: We conducted a meta-analysis and systematic review to evaluate the accuracy of artificial intelligence electrocardiograms in estimating an ejection fraction below 40 %.</div></div><div><h3>Methods</h3><div>We searched PubMed, Embase, and Cochrane Library for studies evaluating the performance of AI ECGs in diagnosing heart failure with reduced ejection fraction. We computed true positives, true negatives, false positives, and false negatives events to estimate pooled sensitivity, specificity, and area under the curve, using R software version 4.3.1, under a random-effects model.</div></div><div><h3>Results</h3><div>We identified 9 studies, including patients with a paired artificial intelligence-enabled electrocardiogram with an echocardiography. patients had an ejection fraction below 40 % according to the echocardiogram. The AI-ECG data yielded areas under the receiver operator of, the sensitivity of), specificity of, and area under the curve of. The mean/median age ranged from 60±9 to 68.05± 11.9 years.</div></div><div><h3>Conclusions</h3><div>In this systematic review and meta-analysis, the use of electrocardiogram-based artificial intelligence models demonstrated high sensitivity and specificity to estimate a left ventricular ejection fraction below 40 %.</div></div>","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 4","pages":"Article 103004"},"PeriodicalIF":3.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Guidelines for Authors","authors":"","doi":"10.1016/S0146-2806(24)00597-8","DOIUrl":"10.1016/S0146-2806(24)00597-8","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 2","pages":"Article 102962"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Information for Readers","authors":"","doi":"10.1016/S0146-2806(24)00592-9","DOIUrl":"10.1016/S0146-2806(24)00592-9","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 2","pages":"Article 102957"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Khaled S Allemailem , Saad Almousa , Mohammed Alissa , Faris Alrumaihi , Hajed Obaid Alharbi , Nahlah Makki Almansour , Leen A. Aldaiji , Amr S. Abouzied , Mahdi H. Alsugoor , Omer Alasmari , Marwh Jamal Albakawi , Jens Stride
{"title":"Innovations in quantitative rapid testing: Early prediction of health risks","authors":"Khaled S Allemailem , Saad Almousa , Mohammed Alissa , Faris Alrumaihi , Hajed Obaid Alharbi , Nahlah Makki Almansour , Leen A. Aldaiji , Amr S. Abouzied , Mahdi H. Alsugoor , Omer Alasmari , Marwh Jamal Albakawi , Jens Stride","doi":"10.1016/j.cpcardiol.2025.103000","DOIUrl":"10.1016/j.cpcardiol.2025.103000","url":null,"abstract":"<div><div>As health monitoring becomes increasingly intricate, the demand for innovative solutions to predict and assess health status is more pressing than ever. This review focuses on the transformative potential of multi-sensor technologies in health monitoring, emphasizing their role in early health status prediction. By integrating diverse sensor types ranging from wearable fitness trackers to implantable devices and environmental monitors healthcare professionals can gain a richer, more nuanced understanding of an individual's physiological state. We analyze various configurations of multi-sensor networks and their efficacy in identifying early indicators of health issues, such as cardiovascular diseases, diabetes, and respiratory ailments. For example, the combination of biometric sensors that track vital signs with environmental data on pollutants can yield invaluable insights into a patient's overall health. This integrated approach not only improves the accuracy of health assessments but also facilitates timely interventions. Furthermore, we address the challenges inherent in multi-sensor systems, including data integration, device interoperability, and the need for advanced algorithms capable of processing complex datasets. Recent advancements in machine learning and artificial intelligence are underscored as pivotal in enhancing the capabilities of these technologies for predictive health analytics. Ultimately, this review highlights how multi-sensor systems can redefine early health status prediction, paving the way for proactive healthcare strategies that significantly improve patient outcomes and optimize healthcare delivery.</div></div>","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 4","pages":"Article 103000"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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