ArticleInternational journal for numerical methods in biomedical engineering2022
Toward an artificial intelligence-assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response.
Article in International journal for numerical methods in biomedical engineering, 2022. 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, 44 citations in OpenAlex.
- Dynamic behavior of the nucleus pulposus within the intervertebral disc loading: a systematic review and meta-analysis exploring the concept of dynamic disc model.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Demystifying machine learning approaches in digital bone imaging using microCT and HRpQCT.Bone reports · 2026Review
- Review
- Digital twins for personalized treatment in uro-oncology in the era of artificial intelligence.Nature reviews. Urology · 2026Review
- A Computational Multiscale Framework for Bone Remodeling: Coupling Apparent Density Evolution and Microscale Shape Optimization.International journal for numerical methods in biomedical engineering · 2025Article
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- Revolutionizing spine surgery with emerging AI-FEA integration.Journal of robotic surgery · 2025Review
- Current progress of digital twin construction using medical imaging.Journal of applied clinical medical physics · 2025Review
- Advantages of digital twin technology in orthopedic trauma Surgery - Exploring different clinical use cases.Scientific reports · 2025Article
- From virtual to reality: innovative practices of digital twins in tumor therapy.Journal of translational medicine · 2025Review
- A Comprehensive AI Framework for Superior Diagnosis, Cranial Reconstruction, and Implant Generation for Diverse Cranial Defects.Bioengineering (Basel, Switzerland) · 2025Article
- Digital Twins for Clinical and Operational Decision-Making: Scoping Review.Journal of medical Internet research · 2025Article
- Article
- Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.Journal of medical Internet research · 2024Article
- Evolution of digital twins in precision health applications: a scoping review study.Research square · 2024Article
- Digital twins for health: a scoping review.NPJ digital medicine · 2024Article
- Digital Twins of Biological Systems: A Narrative Review.IEEE open journal of engineering in medicine and biology · 2024Article
- Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems.Biomimetics (Basel, Switzerland) · 2023Review
- Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems.International journal of spine surgery · 2023Article
- The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article presents an effort toward building an artificial intelligence (AI) assisted framework, coined ReconGAN, for creating a realistic digital twin of the human vertebra and predicting the risk of vertebral fracture (VF). ReconGAN consists of a deep convolutional generative adversarial network (DCGAN), image-processing steps, and finite element (FE) based shape optimization to reconstruct the vertebra model. This DCGAN model is trained using a set of quantitative micro-computed tomography (micro-QCT) images of the trabecular bone obtained from cadaveric samples. The quality of synthetic trabecular models generated using DCGAN are verified by comparing a set of its statistical microstructural descriptors with those of the imaging data. The synthesized trabecular microstructure is then infused into the vertebra cortical shell extracted from the patient's diagnostic CT scans using an FE-based shape optimization approach to achieve a smooth transition between trabecular to cortical regions. The final geometrical model of the vertebra is converted into a high-fidelity FE model to simulate the VF response using a continuum damage model under compression and flexion loading conditions. A feasibility study is presented to demonstrate the applicability of digital twins generated using this AI-assisted framework to predict the risk of VF in a cancer patient with spinal metastasis.
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.