ArticleThe international journal of cardiovascular imaging2020
Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.
Article in The international journal of cardiovascular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence and myocarditis-a systematic review of current applications.Heart failure reviews · 2024Pooled it
- Reproducibility of cardiac volumetric parameters derived from fully automatically prescribed image planes: a direct comparison to manual planning at 1.5-T and 3-T MRI.European radiology · 2026Article
- Neural implicit heart coordinates: 3D cardiac shape reconstruction from sparse segmentations.Medical image analysis · 2026Article
- From Promise to Practice: Real-World Readiness of Artificial Intelligence for Cardiac Magnetic Resonance Imaging.Korean journal of radiology · 2026Article
- Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- Accelerated Cardiac MRI with Deep Learning-based Image Reconstruction for Cine Imaging.Radiology. Cardiothoracic imaging · 2024Article
- Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities.American heart journal plus : cardiology research and practice · 2024Article
- [Artificial intelligence in cardiovascular radiology : Image acquisition, image reconstruction and workflow optimization].Radiologie (Heidelberg, Germany) · 2024Article
- Accurate fully automated assessment of left ventricle, left atrium, and left atrial appendage function from computed tomography using deep learning.European heart journal. Imaging methods and practice · 2024Article
- Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank.European heart journal. Cardiovascular Imaging · 2024Article
- Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data.Scientific reports · 2024Article
- Clinical assessment of an AI tool for measuring biventricular parameters on cardiac MR.Frontiers in cardiovascular medicine · 2024Article
- Automatic segmentation of the great arteries for computational hemodynamic assessment.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging.Pediatric radiology · 2022Review
- Artificial intelligence study on left ventricular function among normal individuals, hypertrophic cardiomyopathy and dilated cardiomyopathy patients using 1.5T cardiac cine MR images obtained by SSFP sequence.The British journal of radiology · 2022Article
- Artificial intelligence and imaging: Opportunities in cardio-oncology.American heart journal plus : cardiology research and practice · 2022Article
- Artificial Intelligence Enabled Fully Automated CMR Function Quantification for Optimized Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Replacement.Journal of interventional cardiology · 2022Article
- Balancing Speed and Accuracy in Cardiac Magnetic Resonance Function Post-Processing: Comparing 2 Levels of Automation in 3 Vendors to Manual Assessment.Diagnostics (Basel, Switzerland) · 2021Article
- Stress CMR in Known or Suspected CAD: Diagnostic and Prognostic Role.BioMed research international · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 years). The most common indications were known or suspected ischemic heart disease, cardiomyopathies or myocarditis. Fully automated analysis of LV volumes and function was performed using a deep learning-based algorithm. The analysis was subsequently corrected by a senior cardiovascular radiologist. Manual volumetric analysis was performed by two radiology trainees. Volumetric results were compared using Bland-Altman statistics and intra-class correlation coefficient. The frequency of clinically relevant differences was analysed using re-classification rates. The fully automated volumetric analysis was completed in a median of 8 s. With expert review and corrections, the analysis required a median of 110 s. Median time required for manual analysis was 3.5 min for a cardiovascular imaging fellow and 9 min for a radiology resident (p < 0.0001 for all comparisons). The correlation between fully automated results and expert-corrected results was very strong with intra-class correlation coefficients of 0.998 for end-diastolic volume, 0.997 for end-systolic volume, 0.899 for stroke volume, 0.972 for ejection fraction and 0.991 for myocardial mass (all p < 0.001). Clinically meaningful differences between fully automated and expert corrected results occurred in 18% of cases, comparable to the rate between the two manual readers (20%). Deep learning-based fully automated analysis of LV volumes and function is feasible, time-efficient and highly accurate. Clinically relevant corrections are required in a minority of cases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.