
Robert AvramUniversité de Montréal | UdeM · Department of Medicine
Robert Avram
MD MSc FRCPC DRCPSC
Director of the HeartWise.ai Laboratory. Interventional Cardiologist at the Montreal Heart Institute.
About
106
Publications
15,101
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Introduction
As an early-career interventional cardiologist at the Montreal Heart Institute and associate professor at the University of Montréal, I lead the artificial intelligence lab HeartWise.ai. Our mission is to develop AI solutions that enhance healthcare professionals' efficiency, diagnostic accuracy, and decision-making abilities.
Additional affiliations
July 2021 - present
Position
- Interventional Cardiologist
Description
- Robert Avram is an interventional cardiologist, AI researcher, and software developer. His research focuses on developing and implementing artificial intelligence algorithms to improve patient care and outcomes in cardiology. Specifically, he is interested in using deep learning techniques to analyze medical images such as coronary angiograms and echocardiograms to aid in diagnosis and treatment planning. He also works on developing software tools for AI-assisted clinical decision support and re
July 2019 - January 2021
Education
June 2014 - July 2017
July 2011 - March 2014
Publications
Publications (106)
Background
Deep learning (DL) applied to electrocardiograms (ECG) is an emerging modality in the prediction of incident atrial fibrillation or atrial flutter (AF). The generalizability of such methods to a tertiary heart center (THC) population has not yet been formally explored.
Purpose
Evaluate the performance of DL in predicting 5-year incident...
Background
Acute assessment of Left Ventricular Ejection Fraction (LVEF) is critical at time of percutaneous coronary intervention to optimize clinical management. The CathEF artificial intelligence algorithm offers a novel approach for real-time, intra-procedural LVEF assessment using routinely obtained left coronary artery angiograms without addi...
Background and Aims
Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS).
Methods
Electrocardiograms in sinu...
Background
Early recognition of volume overload is essential for heart failure patients. Volume overload can often be easily treated if caught early but causes significant morbidity if unrecognized and allowed to progress. Intravascular volume status can be assessed by ultrasound-based estimation of right atrial pressure (RAP), but the availability...
Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit...
The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D mod...
Importance
Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG).
Objective
To develop a deep learning–based neural network for ident...
Aims
The accuracy of voice-assisted technologies, such as Amazon Alexa, to collect data in patients who are older or have heart failure (HF) is unknown. The aim of this study is to analyze the impact of increasing age and comorbid HF, when compared with younger participants and caregivers, and how these different subgroups classify their experience...
[This corrects the article DOI: 10.2196/41209.].
The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D mod...
Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from...
UNSTRUCTURED
The COVID-19 pandemic has disrupted the health care system, limiting health care resources such as the availability of health care professionals, patient monitoring, contact tracing, and continuous surveillance. As a result of this significant burden, digital tools have become an important asset in increasing the efficiency of patient...
Background:
Voice-assisted artificial intelligence (AI)-based systems may streamline clinical care among patients with heart failure (HF) and caregivers; however, randomized clinical trials are needed. We evaluated the potential for Amazon Alexa® (Alexa), a voice-assisted AI-based system, to conduct screening for SARS-CoV2 in a HF clinic.
Methods...
Importance:
Understanding left ventricular ejection fraction (LVEF) during coronary angiography can assist in disease management.
Objective:
To develop an automated approach to predict LVEF from left coronary angiograms.
Design, setting, and participants:
This was a cross-sectional study with external validation using patient data from Decembe...
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Findings, applications and next steps of the cardiovascular proetomic risk prediction model for patients with chronic kidney disease. AUC, area under the receiver-operating characteristic curve; CRIC, Chronic Renal Insufficiency Cohort; ARIC, Atherosclerosis Risk in Communities; CVD, cardiovascular disease; CVE, cardi...
BACKGROUND
Accurate and timely ascertainment of clinical endpoints, particularly hospitalizations, is crucial for clinical trials. The TAILOR-PCI Digital Study extended the main TAILOR-PCI trial's follow-up period (NCT#01742117) to two years, utilizing a smartphone-based research app featuring geofencing-triggered surveys and routine monthly mobile...
Background
Accurate, timely ascertainment of clinical end points, particularly hospitalizations, is crucial for clinical trials. The Tailored Antiplatelet Initiation to Lessen Outcomes Due to Decreased Clopidogrel Response after Percutaneous Coronary Intervention (TAILOR-PCI) Digital Study extended the main TAILOR-PCI trial's follow-up to 2 years,...
Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers’ decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN us...
The acceptability of artificially intelligent interactive voice response (AI-IVR) systems in cardiovascular research settings is unclear. As a result, we evaluated peoples’ attitudes regarding the Amazon Echo Show 8 device when used for electronic data capture in cardiovascular clinics. Participants were recruited following the Voice-Based Screenin...
Balloon entrapment is a potentially fatal complication of percutaneous coronary intervention. This report describes the use of subintimal plaque modification for the management of entrapped balloons. This technique, commonly done during chronic total occlusion angioplasty, was used successfully to retrieve the balloon. (Level of Difficulty: Advance...
BACKGROUND
The COVID-19 pandemic has disrupted the healthcare system, limiting healthcare resources such as the availability of healthcare professionals, patient monitoring, contact tracing, and continuous surveillance. As a result of this significant burden, digital tools can become an important asset in increasing efficiency of patient care deliv...
Background
The COVID-19 pandemic has disrupted the health care system, limiting health care resources such as the availability of health care professionals, patient monitoring, contact tracing, and continuous surveillance. As a result of this significant burden, digital tools have become an important asset in increasing the efficiency of patient ca...
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Background
Peripartum cardiomyopathy (PPCM) is associated with severe morbidity and mortality and the significance of right ventricular (RV) involvement is uncertain. We sought to determine whether RV systolic dysfunction or dilatation is associated with adverse clinical outcomes in women with PPCM.
Methods
We conducted a multi-center...
Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow larg...
Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper...
Atrial Fibrillation (AF) is an important cardiac rhythm disorder, which if left untreated can lead to serious complications such as a stroke. AF can remain asymptomatic, and it can progressively worsen over time; it is thus a disorder that would benefit from detection and continuous monitoring with a wearable sensor. We develop an AF detection algo...
Excess alcohol use is an important determinant of death and disability. Machine learning (ML)-driven interventions leveraging smart-breathalyzer data may help reduce these harms. We developed a digital phenotype of long-term smart-breathalyzer behavior to predict individuals’ breath alcohol concentration (BrAC) levels trained on data from a smart b...
BACKGROUND
TAILOR-PCI was the largest cardiovascular genotype-based randomized clinical trial (RCT) investigating whether CYP2C19 genotype-guided selection of oral P2Y12 inhibitor therapy improved ischemic outcomes after percutaneous coronary intervention (PCI). The TAILOR-PCI Digital Registry was a novel proof-of-concept study that evaluated the f...
Background:
The Tailored Antiplatelet Initiation to Lessen Outcomes Due to Decreased Clopidogrel Response After Percutaneous Coronary Intervention (TAILOR-PCI) Digital Study is a novel proof-of-concept study that evaluated the feasibility of extending the TAILOR-PCI randomized controlled trial (RCT) follow-up period by using a remote digital platf...
Research in artificial intelligence (AI) have progressed over the last decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection and prognostication. This review article is aimed at those without special background in AI. We revi...
Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper...
Objective
Until effective treatments and vaccines are made readily and widely available, preventative behavioural health measures will be central to the SARS-CoV-2 public health response. While current recommendations are grounded in general infectious disease prevention practices, it is still not entirely understood which particular behaviours or...
Importance
Millions of clinicians rely daily on automated preliminary electrocardiogram (ECG) interpretation. Critical comparisons of machine learning–based automated analysis against clinically accepted standards of care are lacking.
Objective
To use readily available 12-lead ECG data to train and apply an explainability technique to a convolutio...
BACKGROUND
Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine learning approaches to develop risk prediction models for incident HF in a cohort of postmenopausal women from the Women's Health Initiative (WHI).
METHODS AND RESULTS
We used two machine learning methods,...
Background
Actigraphy-based measurements of physiologic parameters may enable design of patient-centric heart failure (HF) clinical trials. Recently, the Heart Failure Collaboratory (HFC) focused on recommendations for meaningful change and use of actigraphy as an endpoint in HF clinical trials. We aimed to evaluate randomized controlled trials (RC...
Purpose of Review
With the rising cost of cardiovascular clinical trials, there is interest in determining whether new technologies can increase cost effectiveness. This review focuses on current and potential uses of voice-based technologies, including virtual assistants, in cardiovascular clinical trials.
Recent Findings
Numerous potential uses...
Background:
Elective percutaneous coronary intervention (PCI) is performed to relieve symptoms of angina. Identifying patients who will benefit symptomatically after PCI would be clinically advantageous but robust predictors of symptom resolution are ill-defined.
Methods:
Prospective indexing of baseline angina status, clinical, and procedural c...
Background
In the absence of universal testing, effective therapies, or vaccines, identifying risk factors for viral infection, particularly readily modifiable exposures and behaviors, is required to identify effective strategies against viral infection and transmission.
Methods
We conducted a world-wide mobile application-based prospective cohort...
Aims
Artificial intelligence (A.I) driven voice-based assistants may facilitate data capture in clinical care and trials; however, the feasibility and accuracy of using such devices in a healthcare environment are unknown. We explored the feasibility of using the Amazon Alexa (‘Alexa’) A.I. voice-assistant to screen for risk-factors or symptoms rel...
Coronary heart disease (CHD) is the leading cause of adult death in the United States and worldwide, and for which the coronary angiography procedure is the primary gateway for diagnosis and clinical management decisions. The standard-of-care for interpretation of coronary angiograms depends upon ad-hoc visual assessment by the physician operator....
Background
Consumer devices with broad reach may be useful for screening for atrial fibrillation (AF) in appropriate populations. However, currently there are no consumer devices capable of continuous monitoring for AF.
Objective
To estimate the sensitivity and specificity of a smartwatch algorithm for continuous detection of AF from sinus rhythm...
Background/Introduction
TAILOR-PCI is the largest cardiovascular genotype-based randomized trial (NCT#01742117) investigating whether genotype-guided selection of oral P2Y12 inhibitor therapy improves ischemic outcomes after percutaneous coronary intervention (PCI). The TAILOR-PCI Digital Sub-Study tests the feasibility of extending original follow...
TAILOR-PCI Digital Study tests the feasibility of extending the trial follow-up in a subset of patients for up to 24 months using state-of-the-art digital solutions
•
Successful transition will be measured as enrollment and engagement of at least 80% or more trial participants in the Digital Study
•
The Digital Study will capture hospitalizations...
The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital bi...
Background: In the absence of universal testing, effective therapies, or vaccines, identifying risk factors for viral infection, particularly readily modifiable exposures and behaviors, is required to identify effective strategies against viral infection and transmission.
Methods: We conducted a world-wide mobile application-based prospective cohor...
Introduction
Up to half of coronary artery disease patients will remain symptomatic of angina,
despite coronary revascularization. Moreover, the assessment of angina suffers from the
ischemic threshold adaptation bias where patients will restrict their physical activity level in
order to minimize their angina symptoms. The NOVA-SKIN study (NCT02591...