ArticleFrontiers in medicine2018
Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories.
Article in Frontiers in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 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
69 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational physiological models for individualised mechanical ventilation: a systematic literature review focussing on quality, availability, and clinical readiness.Critical care (London, England) · 2023Pooled it
- Feasibility of large-scale in silico transcatheter aortic valve implantation trials using a fast-to-evaluate model.Biomechanics and modeling in mechanobiology · 2026Article
- Ensuring the quality of in silico evidence: application to medical devices.Briefings in bioinformatics · 2026Article
- Leveraging simulation to provide a practical framework for estimating the novel scope of risk of large language models in healthcare.BMJ mental health · 2026Article
- Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.PLoS computational biology · 2026Article
- Capturing Multiscale Dynamics of Aortic Valve Calcification with a Coupled Fluid-Structure and Systems Biology Model.ACS omega · 2026Article
- Optimized biomechanical design of a tissue engineered pulsatile Fontan conduit.NPJ Regenerative medicine · 2026Article
- Full-field strain measurements on medical devices using digital image correlation: Considerations and practical examples.Journal of the mechanical behavior of biomedical materials · 2025Article
- Digital Twin Models in Atrial Fibrillation: Charting the Future of Precision Therapy?Journal of personalized medicine · 2025Review
- Mechanisms of aortic dissection: From pathological changes to experimental andProgress in materials science · 2025Article
- Computer-generated Clinical Decision-making in the Treatment of Pulmonary Atresia with Intact Ventricular Septum.Cardiovascular engineering and technology · 2025Article
- Defining myocardial fiber bundle architecture in atrial digital twins.Computers in biology and medicine · 2025Article
- Challenges and opportunities in uncertainty quantification for healthcare and biological systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis.Medical & biological engineering & computing · 2025Article
- Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint.JMIR medical informatics · 2025Article
- Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine.NPJ digital medicine · 2025Review
- Verification, validation, and uncertainty quantification of finite element analysis results for pedicle screw assemblies under ASTM F1717 flexion and extension testing.Frontiers in bioengineering and biotechnology · 2025Article
- Innovative Strategies and Advances in Drug Delivery Systems to Address Poor Solubility: A Comprehensive Review.Current drug targets · 2025Review
- G protein-coupled receptor digital twins for precision and personalized medicine.Computational and structural biotechnology journal · 2025Review
- Digital twins in healthcare: a comprehensive review and future directions.Frontiers in digital health · 2025Review
9 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
Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science-the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)-the research arm of CDRH-has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.
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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.