ArticleJournal of the Royal Society, Interface2018
A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
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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
74 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies.Sensors (Basel, Switzerland) · 2023Pooled it
- Capturing the Multiscale Nature of Bone Behavior: Classical, Data-Driven and Hybrid Techniques.Annals of biomedical engineering · 2026Review
- AI-Driven Insights into Glabellar Wrinkle Patterns: Reassessing the Standardised Botulinum Toxin: An Injection Protocol to Address Anatomical Variability.Aesthetic plastic surgery · 2026Article
- prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.Structure (London, England : 1993) · 2026Article
- A Review of Finite Element Analysis in Spine Surgery Decision-Making.Journal of clinical medicine · 2026Review
- Implicit theories of leadership malleability: How beliefs about the malleability of leadership characteristics predict employee voice.PloS one · 2026Article
- Human-centric digital twins in industry 5.0: technologies, applications, and future directions.Frontiers in robotics and AI · 2026Review
- Finite element analysis in dentistry: a comprehensive narrative review of biomechanical principles, clinical applications, and future perspectives.Frontiers in dental medicine · 2026Review
- Risk stratification in intermediate asymptomatic carotid artery disease: a multi-modal approach combining local flow dynamics and systemic clinical profiles.Frontiers in surgery · 2026Article
- Lines of Time: The Evolution of Glabellar Wrinkles over a Century.Aesthetic plastic surgery · 2026Article
- Frontalis Only Contracts in One Direction: AI-Quantum Elasticity and Resistance Gradient Reveals True Nature of Forehead Muscle Movement.Aesthetic plastic surgery · 2025Article
- A comparative study of machine learning methods for predicting mode I and II brittle fracture in notched bend specimens.Scientific reports · 2025Article
- Identification of Adiabatic Temperature Rise Characteristics for Mass Concrete Using the Physics-Informed Neural Network.Materials (Basel, Switzerland) · 2025Article
- A Geometric Deep Learning Model for Real-Time Prediction of Knee Joint Biomechanics Under Meniscal Extrusion.Annals of biomedical engineering · 2025Review
- Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD.Medical & biological engineering & computing · 2025Article
- Finite element modeling of clavicle fracture fixations: a systematic scoping review.Medical & biological engineering & computing · 2025Article
- Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches.Bioengineering (Basel, Switzerland) · 2025Review
- Deep Learning-Based Estimation of Myocardial Material Parameters from Cardiac MRI.Bioengineering (Basel, Switzerland) · 2025Article
- Predicting compressive stress-strain behavior of elasto-plastic porous media via morphology-informed neural networks.Communications engineering · 2025Article
- Helmet material design for mitigating traumatic axonal injuries through AI-driven constitutive law enhancement.Communications engineering · 2025Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.
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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.