Bridging In Silico and In Vitro Validation in AI-Based Vascular Imaging: Lessons from Quantitative Angiography

Bridging In Silico and In Vitro Validation in AI-Based Vascular Imaging: Lessons from Quantitative Angiography | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 October 2025 V1 Latest version Share on Bridging In Silico and In Vitro Validation in AI-Based Vascular Imaging: Lessons from Quantitative Angiography Authors : Elevane Dave 0009-0002-6265-183X [email protected] and Daniel Julius Authors Info & Affiliations https://doi.org/10.22541/au.176132822.23316505/v1 111 views 101 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Artificial intelligence (AI) has become a transformative force in vascular imaging, driving advances in diagnostic precision, image segmentation, and quantitative analysis. However, the credibility of AI-based systems relies heavily on rigorous validation processes that integrate both in silico and in vitro methodologies. Despite notable achievements in computational modeling and digital image analytics, significant translational gaps persist between algorithmic predictions and experimental or clinical outcomes. This article explores how the integration of in silico and in vitro validation strategies can bridge these gaps, particularly through lessons learned from quantitative angiography-an imaging technique that quantifies vascular geometry, flow dynamics, and perfusion characteristics with high fidelity. In the in silico domain, computational phantoms, synthetic datasets, and fluid dynamics simulations serve as foundational tools for testing AI algorithms under controlled conditions. These digital models provide a scalable platform for evaluating system robustness, performing sensitivity analyses, and defining ground-truth benchmarks without ethical or logistical constraints. Conversely, in vitro Supplementary Material File (bridging in silico and in vitro validation in ai-based vascular imaging.pdf) Download 241.39 KB Information & Authors Information Version history V1 Version 1 24 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence digital image physical vascular quantitative angiography vascular geometry vascular imaging Authors Affiliations Elevane Dave 0009-0002-6265-183X [email protected] Department of Computer Engineering, University of Minnesota View all articles by this author Daniel Julius Department of Computer Engineering, University of Minnesota View all articles by this author Metrics & Citations Metrics Article Usage 111 views 101 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Elevane Dave, Daniel Julius. Bridging In Silico and In Vitro Validation in AI-Based Vascular Imaging: Lessons from Quantitative Angiography. Authorea . 24 October 2025. 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