ArticleNature communications2021
Predicting post-operative right ventricular failure using video-based deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Artificial Intelligence and Machine Learning in Left Ventricular Assist Device Implantation: A Systematic Review.Artificial organs · 2025Pooled it
- A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.Circulation · 2026Article
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- A wearable non-invasive sonogenetic pacemaker.Nature biomedical engineering · 2026Article
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- A generalizable deep learning system for cardiac MRI.Nature biomedical engineering · 2026Article
- Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.Annals of biomedical engineering · 2026Review
- Article
- Challenges and opportunities in assessing right ventricular structure and function: a Roadmap for standardization, clinical implementation and research.Nature reviews. Cardiology · 2026Review
- Predicting Simultaneous Heart Kidney Allocation and Posttransplant Adverse Kidney Outcomes.Kidney international reports · 2026Article
- A Multi-Task Deep Learning Model for Pediatric Echocardiography Analysis.medRxiv : the preprint server for health sciences · 2025Article
- Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features.BMC medical imaging · 2025Article
- Role for inflammatory markers in predicting right ventricular failure in mechanical assist device recipients.ESC heart failure · 2025Review
- Digital Therapeutics in Cardiovascular Healthcare: A Narrative Review.Current cardiology reports · 2025Review
- Contemporary applications of artificial intelligence and machine learning in echocardiography.NPJ cardiovascular health · 2025Review
- Prediction of Right Heart Failure in LVAD Candidates: Current Approaches and Future Directions.Journal of cardiovascular development and disease · 2025Review
- Right Ventricular Strain by Echocardiography: Current Clinical Applications and Future Directions for Mechanics Assessment of the Forgotten Ventricle.Journal of personalized medicine · 2025Review
- Formal validation of a deep learning-based automated interpretation system for cardiac structure and function in adult echocardiography.Quantitative imaging in medicine and surgery · 2025Article
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit.Current cardiology reports · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design - automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation.
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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.