Sex-specific assumptions underlie cardiovascular digital twin technologies: A narrative review
4 affiliations
- 1Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centre, University of Amsterdam, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- 2Department of Biomedical Engineering & Physics, Amsterdam UMC, Amsterdam, The Netherlands
- 3Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, The Netherlands
- 4Cardiovascular Biomechanics, Eindhoven University of Technology, Eindhoven, The Netherlands
Abstract
Digital twin technologies (DTTs) are increasingly applied in cardiovascular medicine to support personalized treatment. At the same time, growing evidence demonstrates sex differences in cardiovascular anatomy, physiology, disease presentation, and outcomes. Whether current cardiovascular DTTs adequately incorporate these sex-specific characteristics is unclear. This narrative review examines how sex bias and inclusivity are addressed within cardiovascular DTTs and identifies where sex-related bias may arise within different components of DTT. A six-dimensional digital twin framework is introduced to review DTTs. We focus on three cardiovascular domains: coronary artery disease, aortic valve stenosis, and atrial fibrillation. For each domain, we assessed sex representation in modeling assumptions, data interpretation, clinical outputs, and validation studies. Within three domains, physiological assumptions, boundary conditions, interpretation thresholds, and validation cohorts, models are frequently derived from sex-skewed populations. DTTs estimating noninvasive fractional flow reserve are predominantly validated in male-dominated cohorts, potentially resulting in lower precision in women. In contrast, validation studies for DTTs in TAVI planning show variable sex distributions, with some cohorts being female-skewed and others male-skewed, raising questions about their generalizability across sexes. In both model-based DTTs, sex bias may arise from generalized boundary conditions. Electro-anatomical mapping systems for atrial fibrillation are susceptible to sex bias within measurement methodology. Uniform clinical thresholding and outcome selection bias further add to sex-bias in DTTs. Current cardiovascular DTTs insufficiently account for sex-specific cardiovascular characteristics, risking underperformance in underrepresented populations. Incorporating sex-aware physiological parameters, sex-stratified validation, balanced datasets, and transparent reporting should be considered minimal standards for future clinical implementation. Author summary: Digital twin technologies (DTTs) are virtual models of patients or organs that help personalize cardiovascular treatments, such as diagnosing heart disease or planning valve replacements. However, these technologies often overlook sex-specific differences in heart anatomy, physiology, and disease presentation, despite growing evidence that men and women experience cardiovascular conditions differently. This narrative review introduces a six dimensional framework and explores how sex bias can enter DTTs at every dimension, from data collection to clinical decision-making. Focusing on clinical available DTTs for coronary artery disease, aortic valve stenosis, and atrial fibrillation, we identify opportunities to improve inclusivity. We propose actionable recommendations, including sex-stratified validation, incorporating sex-specific physiological parameters, and documenting limitations transparently to ensure equal cardiovascular care for all patients.
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BibTeX
@article{willigen2026sexspecific,
title = {{Sex-specific assumptions underlie cardiovascular digital twin technologies: A narrative review}},
author = {Bettine G. van Willigen and Henk A. Marquering and Wouter Huberts and Joris R. de Groot},
journal = {PLOS Digital Health},
year = {2026},
month = aug,
doi = {10.1371/journal.pdig.0001692},
url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001692},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Sex-specific assumptions underlie cardiovascular digital twin technologies: A narrative review
AU - Bettine G. van Willigen
AU - Henk A. Marquering
AU - Wouter Huberts
AU - Joris R. de Groot
PY - 2026
DA - 2026/08/31
JO - PLOS Digital Health
DO - 10.1371/journal.pdig.0001692
UR - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001692
AB - Digital twin technologies (DTTs) are increasingly applied in cardiovascular medicine to support personalized treatment. At the same time, growing evidence demonstrates sex differences in cardiovascular anatomy, physiology, disease presentation, and outcomes. Whether current cardiovascular DTTs adequately incorporate these sex-specific characteristics is unclear. This narrative review examines how sex bias and inclusivity are addressed within cardiovascular DTTs and identifies where sex-related bias may arise within different components of DTT. A six-dimensional digital twin framework is introduced to review DTTs. We focus on three cardiovascular domains: coronary artery disease, aortic valve stenosis, and atrial fibrillation. For each domain, we assessed sex representation in modeling assumptions, data interpretation, clinical outputs, and validation studies. Within three domains, physiological assumptions, boundary conditions, interpretation thresholds, and validation cohorts, models are frequently derived from sex-skewed populations. DTTs estimating noninvasive fractional flow reserve are predominantly validated in male-dominated cohorts, potentially resulting in lower precision in women. In contrast, validation studies for DTTs in TAVI planning show variable sex distributions, with some cohorts being female-skewed and others male-skewed, raising questions about their generalizability across sexes. In both model-based DTTs, sex bias may arise from generalized boundary conditions. Electro-anatomical mapping systems for atrial fibrillation are susceptible to sex bias within measurement methodology. Uniform clinical thresholding and outcome selection bias further add to sex-bias in DTTs. Current cardiovascular DTTs insufficiently account for sex-specific cardiovascular characteristics, risking underperformance in underrepresented populations. Incorporating sex-aware physiological parameters, sex-stratified validation, balanced datasets, and transparent reporting should be considered minimal standards for future clinical implementation. Author summary: Digital twin technologies (DTTs) are virtual models of patients or organs that help personalize cardiovascular treatments, such as diagnosing heart disease or planning valve replacements. However, these technologies often overlook sex-specific differences in heart anatomy, physiology, and disease presentation, despite growing evidence that men and women experience cardiovascular conditions differently. This narrative review introduces a six dimensional framework and explores how sex bias can enter DTTs at every dimension, from data collection to clinical decision-making. Focusing on clinical available DTTs for coronary artery disease, aortic valve stenosis, and atrial fibrillation, we identify opportunities to improve inclusivity. We propose actionable recommendations, including sex-stratified validation, incorporating sex-specific physiological parameters, and documenting limitations transparently to ensure equal cardiovascular care for all patients.
SN - 2767-3170
ER -