Deep Learning for Cardiac MRI: Performance Evidence and Barriers to Clinical Integration. A Systematic Review and Meta-Analysis

TY - JOUR

T1 - Deep Learning for Cardiac MRI

T2 - Performance Evidence and Barriers to Clinical Integration. A Systematic Review and Meta-Analysis

AU - Aladwani, Fatemah

AU - Perelli, Alessandro

AU - Mordi, Ify

AU - Khan, Faisel

N1 - Copyright: © The Author(s) 2026. Published by Oxford University Press on behalf of the European Society of Cardiology.

PY - 2026/3/16

Y1 - 2026/3/16

N2 - Aims: This systematic review and meta-analysis aimed to evaluate the current evidence on the use of deep learning in cardiac magnetic resonance imaging, focusing on image segmentation, prediction, and diagnosis. Methods and results: A systematic search of Medline, Web of Science, Embase, and Scopus identified studies published between 2020 and 2025. Eligible studies comprised deep learning-based segmentation, prediction, or diagnosis of cardiac magnetic resonance images. MetaDisc version 1.4 was used for statistical analysis, with a P < 0.05 and an I2 ≥ 75% used as the thresholds for statistical significance and high heterogeneity, respectively. From 1510 retrieved articles, 62 studies met the inclusion criteria, and 12 studies were included in the meta-analysis. Most studies targeted segmentation (n & 45), with fewer addressing diagnosis (n & 9), and prediction (n & 28). Supervised learning predominated (91.94%), and U-Net was the most common architecture (70.97%). Mean Dice score (15 studies) was 0.91 ± 0.03, whereas mean Hausdorff distance (six studies) was 8.99 ± 6.45 mm. Diagnosis and prediction achieved pooled sensitivity of 0.94 (95% CI: 0.92–0.96), specificity of 0.91 (95% CI: 0.89–0.93), and AUC of 0.9831, indicating excellent discriminative ability. Segmentation models reached pooled sensitivity of 1.00 (95% CI: 0.99–1.00) and specificity of 0.98 (95% CI: 0.98–0.99). The AUC from the SROC analysis was 0.9940, confirming exceptional segmentation accuracy. Conclusion: Deep learning models show excellent performance in cardiac magnetic resonance segmentation and diagnosis, often matching or exceeding manual analysis, indicating strong potential for clinical adoption. This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration number CRD42023439659.

AB - Aims: This systematic review and meta-analysis aimed to evaluate the current evidence on the use of deep learning in cardiac magnetic resonance imaging, focusing on image segmentation, prediction, and diagnosis. Methods and results: A systematic search of Medline, Web of Science, Embase, and Scopus identified studies published between 2020 and 2025. Eligible studies comprised deep learning-based segmentation, prediction, or diagnosis of cardiac magnetic resonance images. MetaDisc version 1.4 was used for statistical analysis, with a P < 0.05 and an I2 ≥ 75% used as the thresholds for statistical significance and high heterogeneity, respectively. From 1510 retrieved articles, 62 studies met the inclusion criteria, and 12 studies were included in the meta-analysis. Most studies targeted segmentation (n & 45), with fewer addressing diagnosis (n & 9), and prediction (n & 28). Supervised learning predominated (91.94%), and U-Net was the most common architecture (70.97%). Mean Dice score (15 studies) was 0.91 ± 0.03, whereas mean Hausdorff distance (six studies) was 8.99 ± 6.45 mm. Diagnosis and prediction achieved pooled sensitivity of 0.94 (95% CI: 0.92–0.96), specificity of 0.91 (95% CI: 0.89–0.93), and AUC of 0.9831, indicating excellent discriminative ability. Segmentation models reached pooled sensitivity of 1.00 (95% CI: 0.99–1.00) and specificity of 0.98 (95% CI: 0.98–0.99). The AUC from the SROC analysis was 0.9940, confirming exceptional segmentation accuracy. Conclusion: Deep learning models show excellent performance in cardiac magnetic resonance segmentation and diagnosis, often matching or exceeding manual analysis, indicating strong potential for clinical adoption. This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration number CRD42023439659.

KW - deep learning (DL)

KW - CMR imaging

KW - image segmentation

KW - diagnosis

KW - prediction

KW - cardiovascular disease (CVD)

U2 - 10.1093/ehjimp/qyag045

DO - 10.1093/ehjimp/qyag045

M3 - Article

C2 - 41877690

SN - 2755-9637

VL - 4

JO - European Heart Journal - Imaging Methods and Practice

JF - European Heart Journal - Imaging Methods and Practice

IS - 1

M1 - qyag045

ER -