ArticleScientific reports2023
Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 16 citations in OpenAlex.
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- Predicting adverse outcomes after cardiac surgery and structural interventions: the role of right ventricular function.Future cardiology · 2026Review
- Generalizable deep learning framework for 3D medical image segmentation using limited training data.3D printing in medicine · 2025Article
- Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.Radiology · 2025Review
- Towards a cardiovascular magnetic resonance foundation model for multi-task cardiac image analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging.BJR open · 2025Review
- Artificial Intelligence based fractional flow reserve.Cardiology journal · 2025Review
- Accelerated Cardiac MRI with Deep Learning-based Image Reconstruction for Cine Imaging.Radiology. Cardiothoracic imaging · 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
- Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI.Journal of imaging · 2024Article
- Noninvasive Pressure-Volume Loops Predict Major Adverse Cardiac Events in Heart Failure With Reduced Ejection Fraction.JACC. Advances · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Right ventricular (RV) volumes are commonly obtained through time-consuming manual delineations of cardiac magnetic resonance (CMR) images. Deep learning-based methods can generate RV delineations, but few studies have assessed their ability to accelerate clinical practice. Therefore, we aimed to develop a clinical pipeline for deep learning-based RV delineations and validate its ability to reduce the manual delineation time. Quality-controlled delineations in short-axis CMR scans from 1114 subjects were used for development. Time reduction was assessed by two observers using 50 additional clinical scans. Automated delineations were subjectively rated as (A) sufficient for clinical use, or as needing (B) minor or (C) major corrections. Times were measured for manual corrections of delineations rated as B or C, and for fully manual delineations on all 50 scans. Fifty-eight % of automated delineations were rated as A, 42% as B, and none as C. The average time was 6 min for a fully manual delineation, 2 s for an automated delineation, and 2 min for a minor correction, yielding a time reduction of 87%. The deep learning-based pipeline could substantially reduce the time needed to manually obtain clinically applicable delineations, indicating ability to yield right ventricular assessments faster than fully manual analysis in clinical practice. However, these results may not generalize to clinics using other RV delineation guidelines.
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