Shyam Ramchandani

Toronto, ON, Canada

Last Updated: 09/26/2026

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Found 15 publications

Utility of a novel point-of-care test in detecting coronary artery disease following negative nuclear testing: a case series.

Utility of a novel point-of-care test in detecting coronary artery disease following negative nuclear testing: a case series.

Journal: European heart journal. Case reports
Published: June 19, 2025

Facilitating Earlier Diagnosis of Pulmonary Hypertension Using a Novel Noninvasive Diagnostic.

Facilitating Earlier Diagnosis of Pulmonary Hypertension Using a Novel Noninvasive Diagnostic.

Journal: JACC. Case reports
Published: April 23, 2025

Clinical validation of a machine-learned, point-of-care system to IDENTIFY pulmonary hypertension.

Clinical validation of a machine-learned, point-of-care system to IDENTIFY pulmonary hypertension.

Journal: ERJ open research
Published: December 04, 2024

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease.

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease.

Journal: Journal of visualized experiments : JoVE
Published: August 26, 2024

Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease.

Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease.

Journal: Diagnostics (Basel, Switzerland)
Published: April 02, 2024

Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm.

Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm.

Journal: Diagnostics (Basel, Switzerland)
Published: March 07, 2024

Development of a Non-Invasive Machine-Learned Point-of-Care Rule-Out Test for Coronary Artery Disease.

Development of a Non-Invasive Machine-Learned Point-of-Care Rule-Out Test for Coronary Artery Disease.

Journal: Diagnostics (Basel, Switzerland)
Published: March 07, 2024

Machine Learning Algorithms: Selection of Appropriate Validation Populations for Cardiology Research-Be Careful!

Machine Learning Algorithms: Selection of Appropriate Validation Populations for Cardiology Research-Be Careful!

Journal: JACC. Advances
Published: January 11, 2023

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.

Journal: Frontiers in cardiovascular medicine
Published: June 28, 2022

Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care.

Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care.

Journal: PloS one
Published: March 14, 2022

Machine learning: at the heart of failure diagnosis.

Machine learning: at the heart of failure diagnosis.

Journal: Current opinion in cardiology
Published: January 14, 2021

Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence.

Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence.

Journal: BioMed research international
Published: October 30, 2020
Showing 1-12 of 15

Last Updated: 09/26/2026