A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on behalf of the Stakeholder Taskforce for AI-assisted Robotic Thrombectomy (START)
- URL: http://arxiv.org/abs/2603.28129v1
- Date: Mon, 30 Mar 2026 07:47:32 GMT
- Title: A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on behalf of the Stakeholder Taskforce for AI-assisted Robotic Thrombectomy (START)
- Abstract summary: One of the major medical challenges of the mid-21st century will be the rising prevalence of stroke.<n>One solution for improving timely access to mechanical thrombectomy in geographically diverse populations is the deployment of robotic surgical systems.<n>Our aim was to establish consensus frameworks for developing and validating AI-assisted robots for thrombectomy.
- Score: 29.385482488030686
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While we are making progress in overcoming infectious diseases and cancer; one of the major medical challenges of the mid-21st century will be the rising prevalence of stroke. Large vessels occlusions are especially debilitating, yet effective treatment (needed within hours to achieve best outcomes) remains limited due to geography. One solution for improving timely access to mechanical thrombectomy in geographically diverse populations is the deployment of robotic surgical systems. Artificial intelligence (AI) assistance may enable the upskilling of operators in this emerging therapeutic delivery approach. Our aim was to establish consensus frameworks for developing and validating AI-assisted robots for thrombectomy. Objectives included standardizing effectiveness metrics and defining reference testbeds across in silico, in vitro, ex vivo, and in vivo environments. To achieve this, we convened experts in neurointervention, robotics, data science, health economics, policy, statistics, and patient advocacy. Consensus was built through an incubator day, a Delphi process, and a final Position Statement. We identified that the four essential testbed environments each had distinct validation roles. Realism requirements vary: simpler testbeds should include realistic vessel anatomy compatible with guidewire and catheter use, while standard testbeds should incorporate deformable vessels. More advanced testbeds should include blood flow, pulsatility, and disease features. There are two macro-classes of effectiveness metrics: one for in silico, in vitro, and ex vivo stages focusing on technical navigation, and another for in vivo stages, focused on clinical outcomes. Patient safety is central to this technology's development. One requisite patient safety task needed now is to correlate in vitro measurements to in vivo complications.
Related papers
- In vivo feasibility study of humanoid robots in surgery [4.917769970757841]
Humanoid form factors offer unique potential, particularly for assisting with surgical tasks.<n>Traditionally, robotic systems for surgery are purpose-built platforms such as Intuitive Surgical's da Vinci Surgical System.<n>Here we present a systematic evaluation of contemporary humanoid technology for laparoscopic surgical tasks.
arXiv Detail & Related papers (2026-07-08T22:57:54Z) - Remote Teleoperation of Endovascular Intervention Robots: A Systematic Review [32.26398087308459]
The aim of this review was to determine the evidence regarding teleoperated endovascular robotic systems.<n>We found that teleoperated catheters and guidewires, driven by mechanical or electromagnetic systems, can be navigated across distances up to 7000 km.
arXiv Detail & Related papers (2026-05-21T10:38:11Z) - Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics [131.25998619318702]
We introduce Open-H-Embodiment, the largest open dataset of medical robotic video with synchronized kinematics to date.<n> GR00T-H is the first open foundation vision-language-action model for medical robotics.<n>We train Cosmos-H-Surgical-Simulator, the first action-conditioned world model to enable multi-embodiment surgical simulation.
arXiv Detail & Related papers (2026-04-22T19:05:17Z) - Towards a Medical AI Scientist [73.6056699962416]
We introduce Medical AI Scientist, the first autonomous research framework tailored to clinical autonomous research.<n>The framework operates under 3 research modes, namely paper-based reproduction, literature-inspired innovation, and task-driven exploration.<n>Our system achieves strong alignment between the proposed method and its implementation, while also demonstrating significantly higher success rates in executable experiments.
arXiv Detail & Related papers (2026-03-30T15:37:25Z) - Toward AI Autonomous Navigation for Mechanical Thrombectomy using Hierarchical Modular Multi-agent Reinforcement Learning (HM-MARL) [57.65363326406228]
We propose a Hierarchical Modular Multi-Agent Reinforcement Learning framework for autonomous two-device navigation in vitro.<n>HM-MARL was developed to autonomously navigate a guide catheter and guidewire from the femoral artery to the internal carotid artery (ICA)<n>A modular multi-agent approach was used to decompose the complex navigation task into specialized subtasks, each trained using Soft Actor-Critic RL.<n>In vitro, both HM-MARL models successfully navigated 100% of trials from the femoral artery to the right common carotid artery and 80% to the right ICA but failed on the left-side vessel challenge
arXiv Detail & Related papers (2026-02-20T23:50:35Z) - DINO-CVA: A Multimodal Goal-Conditioned Vision-to-Action Model for Autonomous Catheter Navigation [0.33727511459109777]
This work moves towards autonomous catheter navigation by introducing DINO-CVA, a multimodal goal-conditioned behavior cloning framework.<n>The proposed model fuses visual observations and joystick kinematics into a joint embedding space, enabling policies that are both vision-aware and kinematic-aware.<n>Results show that DINO-CVA achieves high accuracy in predicting actions, matching the performance of a kinematics-only baseline.
arXiv Detail & Related papers (2025-10-19T22:59:32Z) - Autonomous Soft Robotic Guidewire Navigation via Imitation Learning [3.1381624795986345]
In endovascular surgery, interventionists push a thin tube called a catheter, guided by a thin wire to a treatment site inside the patient's blood vessels.<n>Guidewires with robotic tips can enhance maneuverability, but they present challenges in modeling and control.<n>We develop a transformer-based imitation learning framework with goal conditioning, relative action outputs, and automatic contrast dye injections.
arXiv Detail & Related papers (2025-10-10T15:57:09Z) - A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
arXiv Detail & Related papers (2025-08-19T14:41:22Z) - Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions [59.28853595868749]
We introduce two new objectives for future clinical trials that integrate regulatory constraints and treatment policy value for both the entire population and under-served populations.<n>We formulate Randomize First Augment Next (RFAN), a new framework for designing Phase III clinical trials.<n>Our framework consists of a standard randomized component followed by an adaptive one, jointly meant to efficiently and safely acquire and assign patients into treatment arms during the trial.
arXiv Detail & Related papers (2025-03-12T10:17:54Z) - TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [54.98321887435557]
This paper presents a suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design.<n>We provide basic validation methods for each task to ensure the datasets' usability and reliability.<n>We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
arXiv Detail & Related papers (2024-06-30T09:13:10Z) - Artificial Intelligence in the Autonomous Navigation of Endovascular Interventions: A Systematic Review [35.14795071114005]
Autonomous navigation of devices in endovascular interventions can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment.
This systematic review explores recent literature to assess the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous endovascular intervention navigation.
arXiv Detail & Related papers (2024-05-06T09:28:30Z) - RAISE -- Radiology AI Safety, an End-to-end lifecycle approach [5.829180249228172]
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency.
The focus should be on ensuring models meet the highest standards of safety, effectiveness and efficacy.
The roadmap presented herein aims to expedite the achievement of deployable, reliable, and safe AI in radiology.
arXiv Detail & Related papers (2023-11-24T15:59:14Z) - Robotic Navigation Autonomy for Subretinal Injection via Intelligent
Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
We propose a framework for autonomous robotic navigation for subretinal injection.
Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target.
Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
arXiv Detail & Related papers (2023-01-17T21:41:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.