Francisco Lopez-Jimenez

• MD, MSc. FACC, FAHA.
• Chair at Mayo Foundation for Medical Education and Research

Background Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence enhanced ECG (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in cardiac intensive care units. Methods This historical cohort study included consecutive...

Migraine with aura(MwA) is associated with an increased risk of stroke and adverse vascular outcomes compared to those with migraine without aura (MwoA). AI-ECG prediction models developed at our institution can evaluate the probability of atrial fibrillation (AF) and estimate a patient’s age based on a normal sinus rhythm (NSR) ECG. Delta age, AI-...

Background AI-enhanced electrocardiogram (AI-ECG) is a cost-effective tool for left ventricular dysfunction screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, the relatively low positive predictive value (PPV) of many models can lead to high costs from unnecessary fo...

Background Moderate-severe mitral annulus calcification (MAC) and left ventricular (LV) diastolic dysfunction are both linked to increased risks of heart failure and mortality. Assessing LV diastolic function (LVDF) in moderate-severe-MAC by echocardiography is frequently challenging and unreliable. We recently validated an AI-ECG model for LVDF. W...

Background Non-invasive, continuous blood pressure monitoring technologies require additional validation beyond standard cuff-based methods. This study evaluates a non-invasive, multiparametric wearable cuffless blood pressure (BP) diagnostic monitor across all hypertension classes with diverse subjects. Methods A prospective, multicenter study as...

Background There is increasing need for noninvasive biomarkers of Alzheimer’s Disease (AD) neuropathologic change for early detection and intervention through risk‐factor modification and disease‐modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discrimin...

Background Automated data extraction from echocardiography reports could facilitate large-scale registry creation and clinical surveillance of valvular heart diseases (VHD). We evaluated the performance of open-source Large Language Models (LLMs) guided by prompt instructions and chain of thought (CoT) for this task. Methods From consecutive trans...

Background: Left ventricular diastolic dysfunction (LVDD) predicts mortality in cardiac intensive care unit (CICU) patients. A novel artificial intelligence enhanced electrocardiogram (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in the cardiac intensive care unit (CICU). We aim to assess if LVDD...

Background: Our team recently developed an Artificial Intelligence model that enables the identification of increased left ventricular filling pressures using a single-lead electrocardiogram (AI-ECG) with excellent performance (AUC 0.91). However, its ability to predict incident heart failure (HF) in the community is yet to be evaluated. Hypothesis...

Background: Sodium-glucose cotransporter-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA) have known cardiovascular benefits. We aimed to assess the efficacy of GLP1-RA or SGLT2i initiation in reducing mortality or cardiovascular events in a community-based cohort of adults with ischemic stroke. Methods: Patients ≥18 y...

Background: We previously developed deep-learning algorithms that identify coronary artery disease (CAD) risk based on (i) coronary artery calcium (CAC), (ii) obstructive CAD by angiography, and (iii) left ventricular akinesis in ≥1 segment by echocardiogram, using a 12-lead electrocardiogram (CAD ECG-AI). We tested the hypothesis that those with i...

Introduction: Calcium scoring computed tomography (CAC) is a widely available and inexpensive modality of screening for atherosclerosis. This study aims to improve the predictive value of CAC by incorporating pericardial fat characteristics in a CatBoost model. Methods: A sample of 7,000 non-contrast clinically indicated CAC scans were randomly sel...

Introduction: Sleep duration is now recognized as an important determinant of cardiovascular (CV) health. Recently, the CV implications of day-to-day variability in sleep duration have become apparent, with irregular sleep duration associated with increased risk of CV disease, including hypertension. However, the relationship between sleep duration...

Background: AI-ECG is a cost-effective tool for left ventricular dysfunction (LVD) screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, a drawback is low positive predictive value (PPV), which leads to high costs from unnecessary follow-up tests. Therefore, strategies...

Background: Artificial intelligence (AI) can be used to predict a person’s age from the electrocardiogram (ECG) (AI-ECG age) and has been proposed as a measurement of biological age. The difference between AI-ECG age and chronological age (defined as delta-age) is an independent predictor of mortality in the general population. This study assessed...

BACKGROUND Randomized controlled trials (RCTs) of glucagon-like peptide 1 receptor agonists (GLP-1RAs) form the basis for therapeutic recommendations for both males and females. Historically, females have been significantly un- derrepresented in RCTs. OBJECTIVES The authors sought to determine the trends of representation of females in GLP-1RA RCTs...

Background: Risk stratification for suspected pulmonary emboli (PE) remains an area of active research. Current risk stratification algorithms have moderate discriminatory capabilities and are guideline-recommended, but they are poorly utilized in practice, with most clinicians relying predominantly on their clinical training and personal “gestalt....

Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12‐lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel...

Background A subset of individuals with major depressive disorder (MDD) have a high burden of cardiovascular risk factors and cerebral small‐vessel disease, implicating vascular disease in the development of depression. Cross‐sectional studies demonstrate a link between endothelial dysfunction and MDD, but the prospective association between periph...

Background The determination of left ventricular diastolic dysfunction (LVDD) and filling pressure in patients with significant (>moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to detect LVDD and estimate LV filling pressures. Methods This...

Background Heart failure (HF) and atrial fibrillation (AF) are intertwined, sharing common risk factors and influencing each other in a reciprocal manner. While left ventricular diastolic dysfunction (LVDD) stands as a hallmark of HF's hemodynamic profile, its direct correlation with heightened AF risk remains uncertain. Our recent validation of an...

Background Cardiovascular disease remains the leading cause of death and burden globally, warranting the need to explore novel predictors of major adverse cardiovascular events (MACE) using new technologies. Artificial Intelligence (AI)-enhanced ECG (AI-ECG) algorithms can provide information on cardiovascular health of individuals, independent of...

Background The concept of ideal cardiovascular health (CVH) was developed by the American Heart Association (AHA) as a composite of seven health factors and behaviors labeled as the Life’s Simple 7 (LS7). Ideal CVH has been associated with complex measures of cardiovascular aging. We hypothesized that physiologic aging, as determined by a simple, r...

Background Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on ECG remain unknown. Objective To assess the effects of GAHT on ECG changes in TG. Methods Twelve-lead ECGs of TG on GAHT at the Mayo Clinic were inspected using a validated artificial...

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study int...

Aims We aim to determine if our previously validated, diagnostic artificial intelligence (AI) electrocardiogram (ECG) model is prognostic for survival among patients with cardiac amyloidosis (CA). Methods A total of 2533 patients with CA (1834 with light chain amyloidosis (AL), 530 with wild‐type transthyretin amyloid protein (ATTRwt) and 169 with...

Artificial intelligence enabled interpretation of electrocardiogram waveform images (AI-ECG) can identify patterns predictive of future adverse cardiac events. We hypothesized such an approach, which is well described in general medical and surgical patients, would provide prognostic information with respect to the risk of cardiac complications and...

Aims To test whether an index based on the combination of demographics and body volumes obtained with a multisensor 3D body volume (3D-BV) scanner and biplane imaging using a mobile application (myBVI®) will reliably predict the severity and presence of metabolic syndrome (MS). Methods and results We enrolled 1280 consecutive subjects who complete...

Background An artificial intelligence (AI)-based electrocardiogram (ECG) model identifies patients with a higher likelihood of low ejection fraction (EF). Patients with an abnormal AI-ECG score but normal EF (false positives; FP) more often developed future low EF. Objective The purpose of this study was to evaluate echocardiographic characteristi...

AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on p...

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from s...

Aims Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is importa...

Background Extended sedentary behavior is a risk factor for chronic disease and mortality, even among those who exercise regularly. Given the time constraints of incorporating physical activity into daily schedules, and the high likelihood of sitting during office work, this environment may serve as a potentially feasible setting for interventions...

Background Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). CA has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at...

Clinical guidelines recommend influenza vaccination for cardiac patients, and COVID-19 vaccination is also beneficial given their increased risk. Patient education regarding vaccination was developed for cardiac rehabilitation (CR); impact on knowledge and attitudes were evaluated. A single-group pre-post design was applied at a Spanish CR program...

Background Loneliness and social isolation are associated with poor health outcomes such as an increased risk of cardiovascular diseases. Objectives The authors aimed to explore the association between social isolation with biological aging which was determined by artificial intelligence-enabled electrocardiography (AI-ECG) as well as the risk of...

Aims Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results We trained two- and three-dime...

Background Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all‐cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to deter...

Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased...

Background Although several studies have documented the impact of the COVID-19 pandemic on mental health, the long-term effects remain unclear. Aims To examine longitudinal changes in mental health before and during the consecutive COVID-19 waves in a well-established probability sample. Method An online survey was completed by the participants o...

High dose chemotherapy followed by autologous hematopoietic cell transplantation (auto HCT) is a standard therapy for patients with plasma cell disorders (PCD). A barrier to improving candidate selection for auto HCT is appraisal of risk for transplant related complications. Electrocardiography (ECG) is a standard pre-HCT test to evaluate the poten...

Aim Cardiotoxicity is a serious side effect of anthracycline treatment, most commonly manifesting as a reduction in left ventricular ejection fraction (LVEF). Early recognition and treatment have been advocated, but robust, convenient and cost-effective alternatives to cardiac imaging are missing. Recent developments in artificial intelligence (AI)...

Background: Early diagnosis of heart failure with reduced ejection fraction (HFrEF) can lead to reduced healthcare costs and improved outcomes. In this study, we evaluated the economic effectiveness of an AI algorithm for detecting low ejection fraction using 12-lead ECG data. Aims: The primary aim was to estimate the health economic impact of the...

Background: Thoracic aortic aneurysms (TAAs) can be found in 1-2/1000 individuals in the general population and are commonly associated with genetic disorders of connective tissue and bicuspid aortic valve (BAV). Larger size, distal aneurysms, high DBP and presence of BAV are some factors that can accelerate aneurysm growth. Case: A 37-year-old mal...

Background: Atrial septal defects (ASD) and ventricular septal defects (VSD) are among the most common forms of congenital heart disease (CHD). Early detection and intervention for these defects are crucial to prevent complications. Given advancements in AI technology, the development of AI-enabled ECG models for detecting these defects is of signi...

Introduction: Cardiotoxicity is a serious side effect of anthracycline treatment, most commonly manifesting as a reduction in left ventricular ejection fraction (LVEF) on cardiac imaging. Cost-effective alternatives to cardiac imaging are currently not available. Artificial intelligence (AI) models have been developed to detect a reduced LVEF from...

Background: Recently, our team developed an end-to-end artificial intelligence (AI) framework that can successfully estimate left ventricular ejection fraction (LVEF) from echocardiogram videos. However, this framework requires specific views (A2C and PLAX), and cannot estimate LVEF for studies from which these views are missing. Goal: To develop a...

Introduction: Left and right ventricular systolic dysfunction (LVSD and RVSD respectively) contribute to considerable pediatric morbidity. Both LVSD and RVSD can occur in children before symptom onset, emphasizing the need for early detection. Existing deep-learning algorithms can identify LVSD in adults using 12-lead ECG; however, their efficacy i...

Background: Diagnosis of heart failure with preserved ejection fraction (HFpEF) is challenging due to non-diagnostic imaging outputs and discordant clinical features. Using only a single videoclip from a standard transthoracic echocardiogram (TTE), we developed an artificial intelligence (AI) model to differentiate patients with HFpEF from those wi...

Introduction: Metabolically obese normal weight (MONW) refers to metabolic abnormalities despite normal body mass index (BMI). Hypothesis: Because obesity is generally defined by BMI, it is assumed that people with MONW do not have excessive adiposity but still have the metabolic dysregulation associated with adiposity. Methods: We included partici...

Background: Our team has previously developed convolutional neural networks (CNNs) to estimate age and sex from a 10-second, 12-lead ECG as indicators of patient wellness. Here, we develop an additional wellness network to estimate body mass index (BMI) from the ECG input signal. Aims: To evaluate the performance of neural networks trained to class...

Background: The role of body adiposity on metabolic dysregulation in subjects with normal body mass index (BMI) is still under debate with limited population-based evidence. Hypothesis: Body adiposity measured by dual x-ray absorptiometry (DEXA) is associated with metabolic disorders in normal-weight individuals. Methods: We evaluated a sample of a...

Introduction: The effect of obesity on physiologic aging has not been completely established. We previously developed an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm that predicts age and determines that the difference between AI-ECG age and chronological age (Age-Gap) may reflect accelerated aging, as it is associated with...

Background: Enlargement or dilation of the left ventricle (LV) is associated with increased cardiovascular morbidity and mortality, and is an established precursor of ventricular dysfunction and heart failure in almost all major cardiovascular conditions. Although there are existing artificial intelligence frameworks that make use of deep learning...

Introduction: Artificial intelligence applied to electrocardiograms (ECG-AI) enables early and efficient detection of cardiovascular disease. The technical and clinical impact of different approaches to designing ECG-AI remains under-explored. We consider left ventricular hypertrophy (LVH) and examine the impact of disease-specific labeling of mino...

Background: Depression is prevalent in almost half of patients with cardiovascular disease (CVD) and is associated with CVD and mortality. We used an artificial intelligence algorithm using ECG that predicts physiological age (ECG-Age) that has been linked to increased total and CVD mortality and CVD risk factors. We hypothesized that depressive sy...

Introduction: Recently, an artificial intelligence (AI) algorithm was validated for identifying ECG signatures of atrial fibrillation (AF) risk during normal sinus rhythm. Traditionally, socioeconomically disadvantaged populations are less likely to receive novel or guideline-recommended treatments, and have high chronic disease burden, including c...

Introduction: Artificial Intelligence-enabled electrocardiogram (AI-ECG) can estimate the probability of having atrial fibrillation (AFib) while in sinus rhythm. Recurrent stroke in subjects without known AFib may be due to undiagnosed AFib. We evaluated the association between AI-ECG AFib and recurrent ischemic stroke and with incident AFib Method...

Introduction: Left ventricular diastolic function and filling pressure (FP) can be assessed by echocardiography and cardiac catheterization. Recently, a novel artificial intelligence (AI)-enabled ECG has proven to be effective in identifying increased FP determined by echocardiographic assessment of diastolic function. Aims: We aimed to compare a p...

Introduction: Common genetic variation has been linked to an increased risk of developing dilated cardiomyopathy (DCM). However, the phenotypic profile, outcomes, and biological mechanisms by which such genetic background may predispose individuals to DCM are still unknown. Hypothesis: The primary objectives of this study were to develop a DCM poly...

Introduction: Biologic sex and hormonal concentrations shape ECG parameters due to sex hormone effects on cardiac function. AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. Aims: We aimed to test the ability of AI-enabled ECGs to predict pediatric patient sex and explore t...

Background: The gap between electrocardiogram (ECG) artificial intelligence-derived age (ECG-Age) and chronological age (Age-Gap) predicts cardiovascular (CV) comorbidities and mortality reflecting physiologic age. Adverse childhood experiences (ACEs) are traumatic and harmful events that impact adulthood CV outcomes. We hypothesized that ACEs woul...

Background Cine images during coronary angiography contain a wealth of information besides the assessment of coronary stenosis. We hypothesized that deep learning (DL) can discern moderate-severe left ventricular dysfunction among patients undergoing coronary angiography. Objectives The purpose of this study was to assess the ability of machine le...

Background Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal. Methods Using deep electronic he...

BACKGROUND We have previously applied artificial intelligence (AI) to an electrocardiogram (ECG) to detect cardiac amyloidosis (CA). OBJECTIVES In this validation study, the authors observe the postdevelopment performance of the AI-enhanced ECG to detect CA with respect to multiple potential confounders. METHODS Amyloid patients diagnosed after a...

Objective: To identify specific causes of death and determine the prevalence of noncardiovascular (non-CV) deaths in an exercise test referral population while testing whether exercise test parameters predict non-CV as well as CV deaths. Patients and methods: Non-imaging exercise tests on patients 30 to 79 years of age from September 1993 to Dec...

Background: Patients with peripheral arterial disease (PAD) are at increased risk for major adverse cardiac (MACE), limb (MALE) events and all-cause mortality. Developing tools capable of identifying those patients with PAD at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether comp...

Aims: To identify whether adding PMED, a marker of atherosclerosis to established risk scores has an incremental prognostic value for major adverse cardiovascular events (MACE). Methods: This is a retrospective study of patients who underwent measuring peripheral arterial tonometry from 2006 to 2020. The optimal cut-off value of the reactive hyp...

Background Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate. Objectives The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF. Methods...

Background: Despite the epidemic of cardiovascular disease and the benefits of cardiac rehabilitation (CR), availability is known to be insufficient. However, this had not been quantified before the International Council of Cardiovascular Prevention and Rehabilitation’s (ICCPR) Global Audit. This presentation will summarize the results of the Globa...

Background: Cardiovascular rehabilitation (CVR) is a Level 1, Class A standard for comprehensive secondary prevention of cardiovascular disease. Despite well-established benefits, CVR delivery is lacking across the world. Reasons for this dearth have been studied globally, with one of the main issues being lack of programs in low-resource settings...

Obesity remains a major public health problem, affecting almost half of adults in the United States. Increased risk of cardiovascular disease (CVD) and CVD mortality are major obesity-related complications, and management guidelines now recommend weight loss as a key strategy for the primary prevention of CVD in patients with overweight or obesity....

Cardiac rehabilitation has strong evidence of benefit across many cardiovascular conditions but is underused. Even for those patients who participate in cardiac rehabilitation, there is the potential to better support them in improving behaviors known to promote optimal cardiovascular health and in sustaining those behaviors over time. Digital tech...

Introduction: A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. We investigated whether this biomarker is associated with cognitive function. Methods: Using 12-lead electrocardiogram, heart age was estimated for a population-based sample (N = 7779, age 40-85 years, 45.3% men). Associations between...

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UiT The Arctic University of Norway, Northern Norway Regional Health Authority. Background/Introduction Machine learning models have been applied to magnetic resonance images of the brain to estimate brain age gap as a biomarker of bio...

In patients with aortic stenosis, current guidelines recommend valve replacement therapy in case of severe valve narrowing in combination with symptoms and/or left ventricular dysfunction (ejection fraction < 50%). It is increasingly recognized that left ventricular ejection fraction offers a crude interpretation of a complex disease entity that is...