European Heart Journal, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 11, 2024
Language: Английский
Journal of the American College of Cardiology, Journal Year: 2024, Volume and Issue: 84(1), P. 97 - 114
Published: June 24, 2024
Language: Английский
Citations
45
European Heart Journal, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 13, 2025
Abstract Background and Aims Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy predict HF risk. Methods Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), Brazilian Longitudinal Study of Adult (ELSA-Brasil), individuals without baseline were followed for first hospitalization. An AI-ECG model that defines cross-sectional left ventricular systolic dysfunction from 12-lead ECG used, its association with incident evaluated. Discrimination assessed using Harrell’s C-statistic. Pooled cohort equations prevent (PCP-HF) used comparator. Results Among 231 285 YNHHS patients, 4472 had primary hospitalizations over 4.5 years (inter-quartile range 2.5–6.6). UKB ELSA-Brasil, among 42 141 13 454 people, 46 31 developed 3.1 (2.1–4.5) 4.2 (3.7–4.5) years. A positive screen portended 4- 24-fold higher new-onset [age-, sex-adjusted hazard ratio: YNHHS, 3.88 (95% confidence interval 3.63–4.14); UKB, 12.85 (6.87–24.02); 23.50 (11.09–49.81)]. The consistent after accounting comorbidities competing death. Higher probabilities associated progressively Model discrimination 0.718 0.769 0.810 ELSA-Brasil. incorporating PCP-HF yielded significant improvement alone. Conclusions AI single image defined future HF, representing digital biomarker stratifying
Language: Английский
Citations
4
Journal of the American College of Cardiology, Journal Year: 2025, Volume and Issue: 85(12), P. 1302 - 1313
Published: March 24, 2025
Language: Английский
Citations
1
European Journal of Clinical Investigation, Journal Year: 2025, Volume and Issue: 55(S1)
Published: April 1, 2025
Abstract Background The management of cardiotoxicity related to cancer therapies has emerged as a significant clinical challenge, prompting the rapid growth cardio‐oncology. As treatments become more complex, there is an increasing need enhance diagnostic and therapeutic strategies for managing their cardiovascular side effects. Objective This review investigates potential artificial intelligence (AI) revolutionize cardio‐oncology by integrating diverse data sources address challenges management. Methods We explore applications AI in cardio‐oncology, focusing on its ability leverage multiple sources, including electronic health records, electrocardiograms, imaging modalities, wearable sensors, circulating serum biomarkers. Results demonstrated improving risk stratification longitudinal monitoring cardiotoxicity. By optimizing use non‐invasive imaging, biomarkers, facilitates earlier detection, better prediction outcomes, personalized interventions. These advancements are poised patient outcomes streamline decision‐making. Conclusions represents transformative opportunity advancing capabilities. However, successful implementation requires addressing practical such integration, model interpretability, clinician training. Continued collaboration between clinicians developers will be essential fully integrate into routine workflows.
Language: Английский
Citations
1
Diagnostics, Journal Year: 2024, Volume and Issue: 14(17), P. 1839 - 1839
Published: Aug. 23, 2024
The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications risk prediction diagnosis heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts AI machine learning (ML) are explained to provide foundational understanding for those seeking knowledge, supported by examples from literature current practices. We analyze ML methods arrhythmias, failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, embolism, myocardial infarction, comparing their effectiveness both medical perspectives. Special emphasis placed cardiology, including detailed comparisons different methods, identifying most suitable approaches specific analyzing strengths, weaknesses, accuracy, clinical relevance, key findings. Additionally, we explore AI's role emerging field cardio-oncology, particularly managing chemotherapy-induced cardiotoxicity detecting cardiac masses. serves as an insightful guide call action further research collaboration integration aiming enhance precision medicine optimize decision-making.
Language: Английский
Citations
6
Circulation Genomic and Precision Medicine, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 24, 2025
Artificial intelligence is poised to transform cardio-oncology by enabling personalized care for patients with cancer, who are at a heightened risk of cardiovascular disease due both the and its treatments. The rising prevalence cancer availability multiple new therapeutic options has resulted in improved survival among expanded scope not only short-term but also long-term risks resulting from However, there considerable heterogeneity risk, driven nature malignancy as well each individual’s unique characteristics. use novel therapies, such targeted therapies immune checkpoint inhibitors, across groups broadened populations which cardiotoxicity become an important consideration therapy. Therefore, ability understand personalize management key target artificial intelligence, can deduce respond complex patterns within data. These advances necessitate overview established biomarkers spanning advanced imaging, diagnostic testing, multi-omics, evidence supporting their use, proven proposed role refining this attain greater precision prediction cardio-oncologic care.
Language: Английский
Citations
0
Life, Journal Year: 2025, Volume and Issue: 15(3), P. 471 - 471
Published: March 15, 2025
The increasing efficacy of cancer therapies has significantly improved survival rates, but it also highlighted the prevalence cancer-therapy-related cardiac dysfunction (CTRCD). This review provides a comprehensive overview identification, monitoring, and management CTRCD, condition resulting from several treatments, such as anthracyclines, HER2-targeted therapies, target radiotherapy. paper includes discussion mechanisms CTRCD associated with various treatments. Early detection through serum biomarkers advanced imaging techniques is crucial for effective monitoring risk stratification. Preventive strategies include pharmacological interventions ACE inhibitors/angiotensin receptor blockers, beta-blockers, statins. Additionally, novel agents like sacubitril/valsartan, sodium-glucose co-transporter type 2 inhibitors, vericiguat show promise in managing left ventricular dysfunction. Lifestyle modifications, including structured exercise programs optimized nutritional strategies, further contribute to cardioprotection. latest treatments both asymptomatic symptomatic across its stages are discussed. Emerging technologies, genomics, artificial intelligence, biomarkers, gene therapy, paving way personalized approaches prevention treatment. These advancements hold great improving long-term outcomes patients by minimizing cardiovascular complications.
Language: Английский
Citations
0
Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 787 - 787
Published: March 20, 2025
The increasing prevalence of cardiovascular complications in cancer patients due to cardiotoxic treatments has necessitated advanced monitoring and predictive solutions. Cardio-oncology is an evolving interdisciplinary field that addresses these challenges by integrating artificial intelligence (AI) smart cardiac devices. This comprehensive review explores the integration devices cardio-oncology, highlighting their role improving risk assessment early detection real-time cardiotoxicity. AI-driven techniques, including machine learning (ML) deep (DL), enhance stratification, optimize treatment decisions, support personalized care for oncology at risk. Wearable ECG patches, biosensors, AI-integrated implantable enable continuous surveillance analytics. While advancements offer significant potential, such as data standardization, regulatory approvals, equitable access must be addressed. Further research, clinical validation, multidisciplinary collaboration are essential fully integrate solutions into cardio-oncology practices improve patient outcomes.
Language: Английский
Citations
0
JAMA Cardiology, Journal Year: 2025, Volume and Issue: unknown
Published: April 16, 2025
Importance Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable recording single-lead electrocardiograms (ECGs) may enable large-scale community-based assessment. Objective To evaluate whether an artificial intelligence (AI) algorithm can predict HF from noisy ECGs. Design, Setting, and Participants A retrospective cohort study individuals without at baseline was conducted among with conventionally obtained outpatient ECGs in integrated Yale New Haven Health System (YNHHS) prospective population-based cohorts UK Biobank (UKB) Brazilian Longitudinal Study Adult (ELSA-Brasil). Data analysis performed September 2023 to February 2025. Exposure AI-ECG–defined left ventricular systolic dysfunction (LVSD). Main Outcomes Measures Among ECGs, lead I were isolated a noise-adapted AI-ECG model (to simulate ECG signals wearable devices) trained identify LVSD deployed. The association probability new-onset HF, defined as first hospitalization, evaluated. discrimination compared against 2 scores (Pooled Cohort Equations Prevent Heart Failure [PCP-HF] Predicting Risk Cardiovascular Disease Events [PREVENT] equations) using Harrel C statistic, improvement, net reclassification improvement. Results There 192 667 YNHHS patients (median [IQR] age, 56 [41-69] years; 111 181 women [57.7%]), 42 141 UKB participants 65 [59-71] 21 795 [51.7%]), 13 454 ELSA-Brasil 51 [45-58] 7348 [54.6%]) total 3697 (1.9%) developed over median (IQR) 4.6 (2.8-6.6) years, 46 (0.1%) 3.1 (2.1-4.5) 31 (0.2%) 4.2 (3.7-4.5) years. positive screening result associated 3- 7-fold higher each 0.1 increment 27% 65% hazard across cohorts, independent sex, comorbidities, competing death. AI-ECG’s 0.723 (95% CI, 0.694-0.752) YNHHS, 0.736 0.606-0.867) UKB, 0.828 0.692-0.964) ELSA-Brasil. Across incorporating predictions alongside PCP-HF PREVENT equations statistic (difference addition PCP-HF, 0.080-0.107; difference PREVENT, 0.069-0.094). had improvement 0.091 0.205 vs 0.068 0.192 PREVENT; it 18.2% 47.2% 11.8% 47.5% PREVENT. Conclusions Relevance multinational estimated suggesting potential risk-stratification strategy requiring portable devices.
Language: Английский
Citations
0
Future Cardiology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 4
Published: April 18, 2025
Language: Английский
Citations
0