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Chris McIntosh

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Associate ProfessorDepartment of Medical BiophysicsUniversity of Toronto

Chris McIntosh

Associate Professor

Department of Medical Biophysics

Temerty Faculty of Medicine

University of Toronto

Toronto, Ontario, Canada

ORCID

Supervision signals

Professors usually supervise graduate students in their department's programs.
32 publications in the last five years, latest in 2026.
Senior (last) author on 5 recent papers, a sign of leading a research group.
Principal investigator on 2 research grants (2 NSERC), most recently in 2022.

These signals come from public records, including CIHR, NSERC and SSHRC funding decisions. Ask the professor or the graduate program whether they are taking new students.

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Collaborators

Grants

Publications by year

Counts cover the works listed on the university profile; citation counts come from Dimensions.

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Research

Read how they describe their research on their University of Toronto profile.

Latest papers

Machine learning in the prediction of liver iron concentration and iron chelation therapy adjustment.
Hematology (Amsterdam, Netherlands) · 2026
Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction.
JACC. Cardiovascular imaging · 2026
Evaluating the temporal and sociodemographic generalizability of the emergency heart failure mortality risk grade.
ESC heart failure · 2026

All 44 publications

Latest funding

Picosecond Infrared laser mass spectrometry for 10-second determination of various molecular pathologies associated with brain cancers
SSHRC · 2022 · Co-investigator
$121,250
A unifying approach to machine learning driven medical image segmentation
NSERC · 2022 · Principal investigator
$12,500
A unifying approach to machine learning driven medical image segmentation
NSERC · 2022 · Principal investigator
$75,000

All 3 grants and awards

44 publications.

Machine learning in the prediction of liver iron concentration and iron chelation therapy adjustment.

Loh JB, Kim S, Ward R, Sagheb S, Binding A, Kuo KHM, McIntosh C, Jhaveri K

Hematology (Amsterdam, Netherlands)2026

DOI: 10.1080/16078454.2026.2647314 PMID: 41925003

Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction.

Yu C, Peikari M, Labib D, Houbois CP, Steve Fan CP, White JA, Amir E, Hanneman K, J Wintersperger B, Abdel-Qadir H, McIntosh C, Thavendiranathan P

JACC. Cardiovascular imaging2026

DOI: 10.1016/j.jcmg.2026.04.007 PMID: 42212994

Evaluating the temporal and sociodemographic generalizability of the emergency heart failure mortality risk grade.

Abdul-Samad K, Wang X, Austin PC, Abdel-Qadir H, McIntosh C, Gramolini A, Mamdani M, Epelman S, Ross HJ, Lee DS

ESC heart failure2026

DOI: 10.1093/eschf/xvag156 PMID: 42212505

Reply to the Letter to the Editor: Rethinking the clinical utility of machine learning for CTEPH severity.

Grubert Van Iderstine M, Kim S, Karur GR, Granton J, de Perrot M, McIntosh C, McInnis M

European radiology2026

DOI: 10.1007/s00330-025-12151-6 PMID: 41313469

Development and Validation of a Deep Learning-Based Segmentation Method for Fenestration Marker and Graft Body Identification in Fenestrated Endovascular Aortic Repair.

Akouris PP, Kim S, Javidan AP, McIntosh C, Crawford SA

Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists2026

DOI: 10.1177/15266028261437610 PMID: 42017358

Risk Prediction in Cardio-Oncology: Conceptual and Methodological Considerations: JACC: CardioOncology State-of-the-Art Review.

Sen J, Amir E, Austin PC, Marwick TH, McIntosh C, Thavendiranathan P, Abdel-Qadir H

JACC. CardioOncology2026

DOI: 10.1016/j.jaccao.2026.01.006 PMID: 42017562

Remote monitoring of heart failure exacerbations using a smartwatch.

Gao Y, Moayedi Y, Foroutan F, Verma B, Kim B, Luca E, Brum M, Brahmbhatt DH, Duhamel J, Simard A, McIntosh C, Ross HJ

Nature medicine2026

DOI: 10.1038/s41591-026-04247-3 PMID: 41862772

Utility of machine learning for predicting severe chronic thromboembolic pulmonary hypertension based on CT metrics in a surgical cohort.

Grubert Van Iderstine M, Kim S, Karur GR, Granton J, de Perrot M, McIntosh C, McInnis M

European radiology2026

DOI: 10.1007/s00330-025-11972-9 PMID: 40848142

Cancer Genomic Alterations and Microenvironmental Features Encode Synergistic Interactions with Disease Outcomes.

Bayati M, Klein ZP, Bahcheli AT, Slobodyanyuk M, To J, Cheng KCL, Mishra J, Pellegrina D, Guevara-Hoyer K, McIntosh C, Bhat M, Reimand J

Molecular cancer research : MCR2025

DOI: 10.1158/1541-7786.mcr-25-0475 PMID: 40900635

Pretreatment Circulating Vascular Biomarkers Predict Cancer Therapy-Related Cardiac Dysfunction During HER2+ Breast Cancer Treatment.

Gustafson D, Mistry P, Ching C, Nardi-Agmon I, Yu C, Steve Fan CP, Houbois C, Amir E, Marwick T, Abdel-Qadir H, McIntosh C, Thavendiranathan P, Fish JE

JACC. CardioOncology2025

DOI: 10.1016/j.jaccao.2025.09.004 PMID: 41546616

Profile data last refreshed on September 25, 2026 from the university directory, publication records and CIHR, NSERC and SSHRC funding.

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Chris McIntosh

Research

Latest papers

Latest funding

Machine learning in the prediction of liver iron concentration and iron chelation therapy adjustment.

Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction.

Evaluating the temporal and sociodemographic generalizability of the emergency heart failure mortality risk grade.

Reply to the Letter to the Editor: Rethinking the clinical utility of machine learning for CTEPH severity.

Development and Validation of a Deep Learning-Based Segmentation Method for Fenestration Marker and Graft Body Identification in Fenestrated Endovascular Aortic Repair.

Risk Prediction in Cardio-Oncology: Conceptual and Methodological Considerations: JACC: CardioOncology State-of-the-Art Review.

Remote monitoring of heart failure exacerbations using a smartwatch.

Utility of machine learning for predicting severe chronic thromboembolic pulmonary hypertension based on CT metrics in a surgical cohort.

Cancer Genomic Alterations and Microenvironmental Features Encode Synergistic Interactions with Disease Outcomes.

Pretreatment Circulating Vascular Biomarkers Predict Cancer Therapy-Related Cardiac Dysfunction During HER2+ Breast Cancer Treatment.

Picosecond Infrared laser mass spectrometry for 10-second determination of various molecular pathologies associated with brain cancers

A unifying approach to machine learning driven medical image segmentation

A unifying approach to machine learning driven medical image segmentation

Frequent collaborators

Kate Hanneman

Benjamin Haibe-Kains

Paaladinesh Thavendiranathan

Eitan Amir

Yasbanoo Moayedi

Glenn Bauman

Arash Zarrine-Afsar

Ahmed Aman

Michael Brudno

John Granton

Mamatha Bhat

Andrew Hope

Alejandro Berlin

Thomas Purdie

Paul Yip

Giada Sebastiani

Joelle Pineau

Muhammad Mamdani

BO WANG

Farzad Khalvati

Meredith Giuliani