inSteps
The InSteps Stroke platform offers groundbreaking in-silico solutions for stroke treatment. Our unique technology accelerates the development of thrombectomy devices and optimizes clinical trials, giving more patients access to life-saving treatments.
Unmet Medical Need in Stroke
Stroke remains a leading cause of death and disability in Europe, accounting for half a million deaths annually. Despite the introduction of increasingly effective treatments, up to two-thirds of stroke patients remain functionally dependent, underscoring the urgent need for faster and more effective therapeutic options.
Advances in Stroke Treatment Development
The research, development, and evaluation of new stroke treatments are progressing at an unprecedented pace. A key focus lies in the development and optimization of thrombectomy devices, which are designed to improve patient outcomes. The global neuro-thrombectomy market was valued at €500 million in 2020 and is expected to reach €900 million by 2028, reflecting the increasing demand for more advanced solutions.
Clinical Trials and the Future of In-Silico Methods
Clinical trials remain the gold standard for evaluating the efficacy of new stroke treatments. However, fewer than 10% of treatments tested through costly and lengthy trials achieve successful introduction into clinical practice. To address this, regulatory bodies such as the FDA and EMA anticipate that one in seven clinical trials will be supplemented or replaced by in-silico trial solutions.
Regulatory Support for In-Silico Platforms
Our in-silico platform offers key regulatory support by providing robust evidence through simulations such as "what-if" scenarios, risk assessments (e.g., vessel perforation risk, clot fracture), and confirmation of stent performance in rare or under-researched subpopulations. These tools significantly increase the likelihood of regulatory approval and speed up the time to market for novel devices.
What we offer
An in-silico stroke treatment platform that enables virtual testing and optimization of endovascular devices, supporting medical device manufacturers with faster, safer development and launch of next-generation treatments for acute ischemic stroke, improving life-saving outcomes for patients.
In-Silico Models Based on Clinical Patient Data
Our platform is based on real-world data from hundreds of stroke patients and has been validated through both in-vitro experiments and clinical data, with support from research funded by the European Union’s Horizon 2020 program (grant no. 777072) and the INSIST consortium.
The in-silico stroke treatment platform
The in-silico platform for acute ischemic stroke and thrombectomy treatments was developed by a consortium of premier academic institutes and has been extensively validated and peer-reviewed. It represents a state-of-the-art platform uniquely offered by inSteps, specifically engineered for preclinical-to-clinical translation, optimization of clinical trial designs, and regulatory approval support of neuro-thrombectomy devices. It enables comprehensive simulation-based evaluation of stent performance across diverse patient anatomies and clot compositions, allowing novel treatments to be thoroughly assessed and tailored to distinct patient subpopulations prior to costly clinical trials, thereby optimizing their implementation and effectiveness.
Our methodology integrates four core modules:
Module 1
AI-Generated synthetic vessel and thrombus
Geometric deep learning models generate synthetic vessel and thrombus anatomies grounded in real patient data, covering a wide range of anatomical variation.
Thousands of controlled vessel-thrombus combinations generate a scalable virtual patient population with tunable diversity.
Module 2
Finite element model of thrombectomy
A finite element model of thrombectomy procedure simulating stent positioning, deployment, and thrombus retrieval, while quantifying device-thrombus-vessel mechanical interactions.
Module 3
AI-Emulation of thrombectomy
Generative AI neural networks trained on FEM simulations to emulate Module 2 using geometric deep learning. This approach will accelerate large-scale FEM simulations from hours or days to minutes, enabling rapid testing of stent designs across thousands of virtual scenarios and identification of high risk cases in virtual patient populations.
Module 4
Clinical outcome modeling modeling and virtual trial simulation
Predict clinical trial outcomes in-silico by estimating restoration of cerebral perfusion and its effects on brain tissue in virtual AIS patients, and by predicting Modified Rankin Scale and TICI scores based on treatment response.
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Contact us to learn how our platform can help optimize your medical device development and clinical trial designs.
Corporate Address:
1018 WB Amsterdam
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Building Entrance Panama AUMC Location AMC APC Meibergdreef 5 1105 AZ Amsterdam |