Artificial intelligence electrocardiogram as a novel screening tool to detect a newly abnormal left ventricular ejection fraction after anthracycline-based cancer therapy

Independent and Incremental Value of ECG Markers for Prediction of Cancer Therapy-Related Cardiac Dysfunction

BACKGROUND

Strategies to estimate risk of cancer therapy-related cardiac dysfunction (CTRCD) before initiating cardiotoxic cancer treatment are needed. We hypothesized that baseline ECG markers could identify patients at risk for CTRCD.

METHODS AND RESULTS

In this retrospective cohort study, 1278 female patients with stage I-III HER2 (human epidermal growth factor receptor 2)-positive breast cancer meeting the following inclusion criteria were included: baseline ECG with QRS <120 milliseconds, baseline echocardiogram, and ≥1 follow-up echocardiogram. Quantitative measurements of ECG waveform parameters were performed using MUSE (GE Healthcare). The primary outcome of interest was CTRCD at 1 year, defined by left ventricular ejection fraction decline (≥10% to <53% or ≥16% from baseline), or clinical heart failure (New York Heart Association class III/IV). Mean age was 51.7±11.1 years, 990 (77%) received anthracyclines, and all received HER2-targeted therapy. CTRCD occurred in 160 (13%) patients. In a multivariable Cox proportional hazards model adjusting for our previously published CTRCD risk score (composed of patient and treatment-specific factors), 4 ECG markers remained independently associated with CTRCD risk: QRS axis, R-wave duration (lead II), ST segment deviation (lead II), and Sokolow-Lyon voltage (all P<0.05). Compared with a model using only clinical CTRCD risk variables, addition of ECG parameters provided incremental value for predicting CTRCD risk (P<0.001, likelihood ratio test) with continuous net reclassification improvement of 34.9% and integrated discrimination improvement of 3.4%.

CONCLUSIONS

Baseline ECG variables are predictive of subsequent CTRCD and provide incremental value to established clinical risk factors for CTRCD risk classification.

Applications, challenges and future directions of artificial intelligence in cardio-oncology

BACKGROUND

The management of cardiotoxicity related to cancer therapies has emerged as a significant clinical challenge, prompting the rapid growth of cardio-oncology. As cancer treatments become more complex, there is an increasing need to enhance diagnostic and therapeutic strategies for managing their cardiovascular side effects.

OBJECTIVE

This review investigates the potential of artificial intelligence (AI) to revolutionize cardio-oncology by integrating diverse data sources to address the challenges of cardiotoxicity management.

METHODS

We explore applications of AI in cardio-oncology, focusing on its ability to leverage multiple data sources, including electronic health records, electrocardiograms, imaging modalities, wearable sensors, and circulating serum biomarkers.

RESULTS

AI has demonstrated significant potential in improving risk stratification and longitudinal monitoring of cardiotoxicity. By optimizing the use of electrocardiograms, non-invasive imaging, and circulating biomarkers, AI facilitates earlier detection, better prediction of outcomes, and more personalized therapeutic interventions. These advancements are poised to enhance patient outcomes and streamline clinical decision-making.

CONCLUSIONS

AI represents a transformative opportunity in cardio-oncology by advancing diagnostic and therapeutic capabilities. However, successful implementation requires addressing practical challenges such as data integration, model interpretability, and clinician training. Continued collaboration between clinicians and AI developers will be essential to fully integrate AI into routine clinical workflows.

AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring

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) and smart cardiac devices. This comprehensive review explores the integration of artificial intelligence (AI) and smart cardiac devices in cardio-oncology, highlighting their role in improving cardiovascular risk assessment and the early detection and real-time monitoring of cardiotoxicity. AI-driven techniques, including machine learning (ML) and deep learning (DL), enhance risk stratification, optimize treatment decisions, and support personalized care for oncology patients at cardiovascular risk. Wearable ECG patches, biosensors, and AI-integrated implantable devices enable continuous cardiac surveillance and predictive analytics. While these advancements offer significant potential, challenges such as data standardization, regulatory approvals, and equitable access must be addressed. Further research, clinical validation, and multidisciplinary collaboration are essential to fully integrate AI-driven solutions into cardio-oncology practices and improve patient outcomes.

Long-term Follow-up for Survivors of Childhood Cancer in Saudi Arabia: A Multicenter Cross-Sectional Study

Background

With the advancement of childhood cancer therapy, long-term survivors are on the rise. Reports on childhood cancer survivors in Saudi Arabia are scarce. This study aims to assess the spectrum and burden of long-term complications among survivors of childhood cancer in Saudi Arabia.

Methods

This cross-sectional study, conducted at multiple cancer centers in Saudi Arabia, enrolled survivors who had been diagnosed with cancer before the age of 14 and had completed at least 5 years after completion of cancer therapy. The primary outcome was to estimate the prevalence of chronic health conditions (CHC) among these survivors. The secondary outcome was to assess the impact of primary cancer diagnosis and cancer therapies on the occurrence of CHC.

Results

A total of 305 survivors met the inclusion criteria as of July 2022. Females were 165 participants. The median follow-up and age at evaluation were 8.5 and 14 years, respectively. Leukemia was the most common cancer type (49.3%), followed by lymphoma (16.7%) and solid tumors (15.7%). Chemotherapy was administered to 287 survivors. Radiotherapy and surgery were used in 29.2% and 22.3% of cases, respectively. Seventy-eight percent of participants experienced at least 1 CHC, with 31.1% and 14.2% having 2 and 3 CHC, respectively. A multivariate logistic regression identified significant association between CHC and solid tumors compared to hematological malignancies (OR 2.2; 95% CI: 1.1-4.3; P = .023). Growth impairment was the most common CHC, followed by endocrinopathy. Radiotherapy was significantly associated with short stature (95% CI: 1.2-3.6; P = .008). The majority of CHC, 77.3%, were mild in severity, while 19.3% were moderate, 2.9% were severe, and .5% were life-threatening.

Conclusion

The long-term complications of childhood cancer have revealed a prevalent concern. To optimize health outcomes, it is essential to implement well-structured and long-term follow-up tailored to risk profiles, utilize cost-effective screening methods, and promote prospective clinical research and establishment of a registry.

Diagnosis of cancer therapy-related cardiovascular toxicities: A multimodality integrative approach and future developments

Diagnosing cancer therapy-related cardiovascular toxicities may be a challenge. The interplay between cancer and cardiovascular diseases, beyond shared cardiovascular and cancer risk factors, and the increasingly convoluted cancer therapy schemes have complicated cardio-oncology. Biomarkers used in cardio-oncology include serum, imaging and rhythm modalities to ensure proper diagnosis and prognostic stratification of cardiovascular toxicities. For now, troponin and natriuretic peptides, multimodal cardiovascular imaging (led by transthoracic echocardiography combined with cardiac magnetic resonance or computed tomography angiography) and electrocardiography (12-lead or Holter monitor) are cornerstones in cardio-oncology. However, the imputability of cancer therapies is sometimes difficult to assess, and more refined biomarkers are currently being studied to increase diagnostic accuracy. Advances reside partly in pathophysiology-based serum biomarkers, improved cardiovascular imaging through new technical developments and remote monitoring for rhythm disorders. A multiparametric omics approach, enhanced by deep-learning techniques, should open a new era for biomarkers in cardio-oncology in the years to come.

Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review

PURPOSE OF REVIEW

This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications in cancer patients. It also highlights multi-omics analysis, explainable AI, and real-time decision-making, while addressing challenges like data heterogeneity and ethical concerns.

RECENT FINDINGS

AI can advance cardio-oncology by leveraging imaging, electronic health records (EHRs), electrocardiograms (ECG), and multi-omics data for early cardiotoxicity detection, stratification and long-term risk prediction. Novel AI-ECG models and imaging techniques improve diagnostic accuracy, while multi-omics analysis identifies biomarkers for personalized treatment. However, significant barriers, including data heterogeneity, lack of transparency, and regulatory challenges, hinder widespread adoption. AI significantly enhances early detection and intervention in cardio-oncology. Future efforts should address the impact of AI technologies on clinical outcomes, and ethical challenges, to enable broader clinical adoption and improve patient care.

A Resting ECG Screening Protocol Improved with Artificial Intelligence for the Early Detection of Cardiovascular Risk in Athletes

Background/Objectives: This study aimed to evaluate an artificial intelligence (AI)-enhanced electrocardiogram (ECG) screening protocol for improved accuracy, efficiency, and risk stratification across six sports: handball, football, athletics, weightlifting, judo, and karate. Methods: For each of the six sports, resting 12-lead ECGs from healthy children and junior athletes were analyzed using AI algorithms trained on annotated datasets. Parameters included the QTc intervals, PR intervals, and QRS duration. Statistical methods were used to examine each sport's specific cardiovascular adaptations and classify cardiovascular risk predictions as low, moderate, or high risk. Results: The accuracy, sensitivity, specificity, and precision of the AI system were 97.87%, 75%, 98.3%, and 98%, respectively. Among the athletes, 94.54% were classified as low risk and 5.46% as moderate risk with AI because of borderline abnormalities like QTc prolongation or mild T-wave inversions. Sport-specific trends included increased QRS duration in weightlifters and low QTc intervals in endurance athletes. Conclusions: The statistical analyses and the AI-ECG screening protocol showed high precision and scalability for the proposed athlete cardiovascular health risk status stratification. Additional early detection research should be conducted further for diverse cohorts of individuals engaged in sports and explore other diagnostic methods that can help increase the effectiveness of screening.

Applications of Artificial Intelligence for the Prediction and Diagnosis of Cancer Therapy-Related Cardiac Dysfunction in Oncology Patients

Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.

Cardiotoxicity of Anthracyclines

Anthracycline chemotherapy is associated with cardiotoxicity, predominantly manifesting as left ventricular systolic dysfunction within the first year of treatment. Early detection is possible through biomarkers and cardiovascular imaging before clinical symptoms develop. Comprehensive cardiovascular risk assessment is essential for all patients prior to anthracycline therapy to stratify their risk of cardiotoxicity. Preventive measures, including cardiovascular risk optimization, as well as anthracycline dose adjustments, the use of liposomal anthracyclines, and dexrazoxane in high-risk patients, are crucial to mitigate the risk of cardiotoxicity. Long-term follow-up and cardiovascular risk optimization are critical for cancer survivors to optimize cardiovascular outcomes.

Anthracycline-induced cardiomyopathy: risk prediction, prevention and treatment

Anthracyclines are the cornerstone of treatment for many malignancies. However, anthracycline cardiotoxicity is a considerable concern given that it can compromise the clinical effectiveness of the treatment and patient survival despite early discontinuation of therapy or dose reduction. Patients with cancer receiving anthracycline treatment can have a reduction in their quality of life and likelihood of survival due to cardiotoxicity, irrespective of their oncological prognosis. Increasing knowledge about anthracycline cardiotoxicity has enabled the identification of patients who are candidates for anthracycline regimens and those who might develop anthracycline-induced cardiomyopathy. Anthracycline cardiotoxicity is a unique and evolving phenomenon that begins with myocardial cell damage, progresses to reduced left ventricular ejection fraction, and culminates in symptomatic heart failure if it is not promptly detected and treated. Early risk stratification can be guided by imaging or biomarkers. In this Review, we present a comprehensive and clinically useful approach to cardiomyopathy related to anthracycline therapy, encompassing its epidemiology, definition, mechanisms, novel classifications, risk factors and patient risk stratification, diagnostic approaches (including imaging and biomarkers), treatment guidelines algorithms, and the role of new cardioprotective drugs that are used for the treatment of heart failure.

Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images

BACKGROUND

Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to ECG images as a surrogate for imaging risk biomarkers and its association with early CTRCD.

METHODS

Across a US-based health system (2013-2023), we identified 1550 patients (aged, 60 [interquartile range, 51-69] years, 1223 [78.9%] women) without cardiomyopathy who received anthracyclines or trastuzumab for breast cancer or non-Hodgkin lymphoma and had ECG performed ≤12 months before treatment. We deployed a validated AI model of left ventricular systolic dysfunction to baseline ECG images and defined low-, intermediate-, and high-risk groups based on AI-ECG left ventricular systolic dysfunction probabilities of <0.01, 0.01 to 0.1, and ≥0.1 (positive screen), respectively. We explored the association with early CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction <50%), or left ventricular ejection fraction <40%, up to 12 months after treatment. In a mechanistic analysis, we assessed the association between global longitudinal strain and AI-ECG left ventricular systolic dysfunction probabilities in studies performed within 15 days of each other.

RESULTS

Among 1550 patients without known cardiomyopathy (median follow-up, 14.1 [interquartile range, 13.4-17.1] months), 83 (5.4%), 562 (36.3%), and 905 (58.4%) were classified as high, intermediate, and low risk, respectively, by baseline AI-ECG. A high-risk versus low-risk AI-ECG screen (≥0.1 versus <0.01) was associated with a 3.4-fold and 13.5-fold higher incidence of CTRCD (adjusted hazard ratio, 3.35 [95% CI, 2.25-4.99]) and left ventricular ejection fraction <40% (adjusted hazard ratio, 13.52 [95% CI, 5.06-36.10]), respectively. Post hoc analyses supported longitudinal increases in AI-ECG probabilities within 6 to 12 months of a CTRCD event. Among 1428 temporally linked echocardiograms and ECGs, AI-ECG left ventricular systolic dysfunction probabilities were associated with worse global longitudinal strain (global longitudinal strain, -19% [interquartile range, -21% to -17%] for probabilities <0.1, to -15% [interquartile range, -15% to -9%] for ≥0.5 [P<0.001]).

CONCLUSIONS

AI applied to baseline ECG images can stratify the risk of early CTRCD associated with anthracycline or trastuzumab exposure in the setting of breast cancer and non-Hodgkin lymphoma therapy.

Evolving therapeutics and ensuing cardiotoxicities in triple-negative breast cancer

Defined as scarce expression of hormone receptors and human epidermal growth factor receptor 2, triple-negative breast cancer (TNBC) is labeled as the most heterogeneous subtype of breast cancer with poorest prognosis. Despite rapid advancements in precise subtyping and tailored therapeutics, the ensuing cancer therapy-related cardiovascular toxicity (CTR-CVT) could exert detrimental impacts to TNBC survivors. Nowadays, this interdisciplinary issue is incrementally concerned by cardiologists, oncologists and other pertinent experts, propelling cardio-oncology as a booming field focusing on the whole-course management of cancer patients with potential cardiovascular threats. Here in this review, we initially profile the evolving molecular subtyping and therapeutic landscape of TNBC. Further, we introduce various monitoring approaches of CTR-CVT. In the main body, we elaborate on typical cardiotoxicities ensuing anti-TNBC treatments in detail, ranging from chemotherapy (especially anthracyclines), surgery, anesthetics, radiotherapy to immunotherapy, with future perspectives on promising directions in the era of artificial intelligence and traditional Chinese medicine.

Hypothesis paper: GDF15 demonstrated promising potential in Cancer diagnosis and correlated with cardiac biomarkers

BACKGROUND

Cardiovascular toxicity represents a significant adverse consequence of cancer therapies, yet there remains a paucity of effective biomarkers for its timely monitoring and diagnosis. To give a first evidence able to elucidate the role of Growth Differentiation Factor 15 (GDF15) in the context of cancer diagnosis and its specific association with cardiac indicators in cancer patients, thereby testing its potential in predicting the risk of CTRCD (cancer therapy related cardiac dysfunction).

METHODS

Analysis of differentially expressed genes (DEGs), including GDF15, was performed by utilizing data from the public repositories of the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Cardiomyopathy is the most common heart disease and its main clinical manifestations, such as heart failure and arrhythmia, are similar to those of CTRCD. Examination of GDF15 expression was conducted in various normal and cancerous tissues or sera, using available database and serum samples. The study further explored the correlation between GDF15 expression and the combined detection of cardiac troponin-T (c-TnT) and N-terminal prohormone of brain natriuretic peptide (NT-proBNP), assessing the combined diagnostic utility of these markers in predicting risk of CTRCD through longitudinal electrocardiograms (ECG).

RESULTS

GDF15 emerged as a significant DEG in both cancer and cardiomyopathy disease models, demonstrating good diagnostic efficacy across multiple cancer types compared to healthy controls. GDF15 levels in cancer patients correlated with the established cardiac biomarkers c-TnT and NT-proBNP. Moreover, higher GDF15 levels correlated with an increased risk of ECG changes in the cancer cohort.

CONCLUSION

GDF15 demonstrated promising diagnostic potential in cancer identification; higher GDF15, combined with elevated cardiac markers, may play a role in the monitoring and prediction of CTRCD risk.

AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions

The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI's role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making.

Application of Artificial Intelligence in Cardiology: A Bibliometric Analysis

Recent advancements in artificial intelligence (AI) applications in medicine have been significant over the past 30 years. To monitor current research developments, it is crucial to examine the latest trends in AI adoption across various medical fields. This bibliometric analysis focuses on AI applications in cardiology. Unlike existing literature reviews, this study specifically examines journal articles published in the last decade, sourced from both Scopus and Web of Science databases, to illustrate the recent trends in AI within cardiology. The bibliometric analysis involves a statistical and quantitative evaluation of the literature on AI application in cardiovascular medicine over a defined period. A comprehensive global literature review is conducted to identify key research areas, authors, and their interrelationships through published works. The leading institutions and most influential authors in research on the role of AI in cardiology were located in the United States, the United Kingdom, and China. This study also provides researchers with an overview of the evolution of research in AI and cardiology. The main contribution of this study is to highlight the prominent authors, countries, journals, institutions, keywords, and trends in the development of AI in cardiology.

A Systematic Review of the Cardiotoxic Effects of Targeted Therapies in Oncology

Cancer therapy advancements have improved survival rates but also introduced significant cardiotoxic risks. Cardiotoxicity, a critical adverse effect of cancer treatments such as doxorubicin, trastuzumab, and radiotherapy, poses substantial challenges. This systematic review synthesizes findings from studies on cardiotoxicity induced by cancer therapies, focusing on detection and management. Key predictors of chemotherapy-induced myocardial toxicity (CIMT) include advanced age, hypertension, hyperlipidemia, diabetes, and elevated N-terminal pro-B-type natriuretic peptide levels. Regular echocardiographic assessments, particularly of the left ventricular global longitudinal strain (LVGLS) and left ventricular ejection fraction (LVEF), are essential for early detection. The CardTox-Score, incorporating these risk factors, shows high sensitivity and specificity in predicting CIMT. Advanced imaging techniques and biomarkers play crucial roles in identifying at-risk patients before functional decline. Early biomarkers and imaging techniques such as LVGLS and LVEF are effective in diagnosing and managing cardiotoxicity, allowing timely interventions. Cardiology involvement in patient care significantly enhances adherence to cardiac monitoring guidelines and reduces cardiotoxicity risks. Management strategies emphasize regular cardiac monitoring, patient education, and the use of cardioprotective agents. A collaborative approach between cardiologists and oncologists is vital to assess cardiovascular risks, minimize vascular toxicity, and manage long-term adverse effects, ensuring the safety and efficacy of cancer therapies. This review underscores the importance of early detection and proactive management of cardiotoxicity in cancer patients to optimize treatment outcomes and improve quality of life.

Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images

Background

Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability.

Objectives

To examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging risk biomarkers, and its association with CTRCD.

Methods

Across a five-hospital U.S.-based health system (2013-2023), we identified patients with breast cancer or non-Hodgkin lymphoma (NHL) who received anthracyclines (AC) and/or trastuzumab (TZM), and a control cohort receiving immune checkpoint inhibitors (ICI). We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to ECG images (≥0.1, positive screen) and explored its association with i) global longitudinal strain (GLS) measured within 15 days (n=7,271 pairs); ii) future CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction [LVEF]<50%), and LVEF<40%. In the ICI cohort we correlated baseline AI-ECG-LVSD predictions with downstream myocarditis.

Results

Higher AI-ECG LVSD predictions were associated with worse GLS (-18% [IQR:-20 to -17%] for predictions<0.1, to -12% [IQR:-15 to -9%] for ≥0.5 (p<0.001)). In 1,308 patients receiving AC/TZM (age 59 [IQR:49-67] years, 999 [76.4%] women, 80 [IQR:42-115] follow-up months) a positive baseline AI-ECG LVSD screen was associated with ~2-fold and ~4.8-fold increase in the incidence of the composite CTRCD endpoint (adj.HR 2.22 [95%CI:1.63-3.02]), and LVEF<40% (adj.HR 4.76 [95%CI:2.62-8.66]), respectively. Among 2,056 patients receiving ICI (age 65 [IQR:57-73] years, 913 [44.4%] women, follow-up 63 [IQR:28-99] months) AI-ECG predictions were not associated with ICI myocarditis (adj.HR 1.36 [95%CI:0.47-3.93]).

Conclusion

AI applied to baseline ECG images can stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure.