The carbon cost of animal trials and how digital twins can help

The Carbon Cost of Animal Testing — And How Digital Twins Can Help

The medical device industry has long measured progress in years saved, lives improved, and regulatory hurdles cleared. But there is another metric that rarely appears on development dashboards: the carbon footprint of the testing process itself. When we talk about making healthcare more sustainable, the conversation usually focuses on hospitals, …

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Accelerating In Silico Medical Innovation: Virtonomy Transforms Healthcare Simulation enabled by NVIDIA

The medical device industry faces many challenges and pressures to reduce development timelines, cut costs and navigate complex regulatory pathways. Traditional development approaches rely heavily on animal testing and extensive clinical trials – processes that can take years to complete and many iteration loops due to failed trials. With new, …

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virtonomy webinar pediatric in silico data

Webinar recording: Transforming Pediatric Cardiovascular Device Development with In Silico Trials

Pediatric cardiovascular medical device development faces a critical challenge: children are not just “small adults.” Their unique anatomies, rapid growth, and vulnerable physiology create hurdles that traditional research and testing methods struggle to overcome. Small patient populations, ethical limits on clinical trials, and high development costs leave most companies focusing …

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5 features in v-patients to help you design devices that fit more patients

5 Features in v-Patients That Help You Design Devices that Fit More Patients

When One Size Doesn’t Fit All Medical devices should work for everyone—not just for a narrow group of patients. True inclusive medical device design means creating solutions that fit people across age, sex, size, and anatomy. Yet too often, devices are designed and tested on limited datasets, which can lead …

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Simon Sonntag showing digital twinning software v-Patients on computer

What to Expect When Working with Virtonomy: A Guide to Your Digital Twin Journey

Bringing digital twinning software into your medical device development process can seem complex at first. The technology might feel abstract, and introducing new software into your workflow may put you off. However, our team of experts makes sure you are guided through the whole process and can start using the …

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a person working on computer with FDA logo on the screen

Debunking Myths: What’s Holding Back In Silico Innovation in Cardiovascular Devices

As regulatory frameworks evolve and digital health technologies advance, in silico methods like modeling, simulation, and virtual clinical trials are increasingly positioned to transform cardiovascular device development. In silico clinical trials for cardiovascular devices are high on the list of priorities for regulators such as FDA. Still, despite clear advantages, …

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How Digital Twins Are Transforming Healthcare

Key Insights from the Digital.Health Webinar Digital twin technology is rapidly reshaping the healthcare landscape, offering new ways to optimize medical treatments, device development, and patient care. In a recent Digital.Health webinar, industry experts, including Ami Bhatt (Chief Innovation Officer at American College of Cardiology), Simon Sonntag (Co-Founder & CEO …

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Customer Success Story: Developing a Total Artificial Heart (TAH) with v-Patients

Read our interview with Pinja Ojanen, Research Nurse at Scandinavian Realheart. Since 2019, Scandinavian Realheart has partnered with Virtonomy, using the v-Patients software to develop their Total Artificial Heart (TAH) for patients with severe heart failure. The v-Patients platform enables Realheart to test and refine the TAH’s anatomical fit in …

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black female doctor working on a digital twin software

From the data lab: Automated Device Positioning for Optimal Device Fit in Digital Twins

Automated Device Positioning: In our in-silico medical device testing practice what often happens is that we are presented with extensive patient cohort data. The first step often involves segmenting out all the relevant anatomies using a range of automatic to semi-automatic algorithms. What we end up with at this stage …

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