ArticleNature machine intelligence2019
Deep learning cardiac motion analysis for human survival prediction.
Article in Nature machine intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
103 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges.Vascular health and risk management · 2026Pooled it
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale.Scientific reports · 2026Article
- Diagnostic Accuracy of Automated Pneumothorax Detection Via Novice-Acquired Ultrasound After Chest Tube Removal: Comparison With Expert Interpretation and Chest Radiography.CHEST pulmonary · 2026Article
- From Promise to Practice: Real-World Readiness of Artificial Intelligence for Cardiac Magnetic Resonance Imaging.Korean journal of radiology · 2026Article
- 3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.NPJ digital medicine · 2026Article
- Synthetic X‑ray‑driven tracking and control of miniature medical devices.Nature machine intelligence · 2026Article
- Adaptive clustering for medical image analysis using the improved separation index.Scientific reports · 2025Article
- Machine learning-assisted point-of-care diagnostics for cardiovascular healthcare.Bioengineering & translational medicine · 2025Review
- ChatGPT-4-Driven Liver Ultrasound Radiomics Analysis: Diagnostic Value and Drawbacks in a Comparative Study.JMIR AI · 2025Article
- Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.IEEE transactions on big data · 2025Article
- Bppv nystagmus signals diagnosis framework based on deep learning.Physical and engineering sciences in medicine · 2025Article
- Automated Bi-Ventricular Segmentation and Regional Cardiac Wall Motion Analysis for Rat Models of Pulmonary Hypertension.Pulmonary circulation · 2025Article
- Voiceprints of cognitive impairment: analyzing digital voice for early detection of Alzheimer's and related dementias.NPJ dementia · 2025Article
- Precision Medicine for Pulmonary Vascular Disease: The Future Is Now (2023 Grover Conference Series).Pulmonary circulation · 2025Article
- A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.Nature machine intelligence · 2025Article
- Semi-stationary Multi-source AI-powered Real-time Tomography.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Automated inversion time selection for late gadolinium-enhanced cardiac magnetic resonance imaging.European radiology · 2024Article
- A clinical trial termination prediction model based on denoising autoencoder and deep survival regression.Quantitative biology (Beijing, China) · 2024Article
43 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4D
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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.