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

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.

Figures (5)

• Figure 1: Flowchart for methods followed in this study, including incubator meeting, three rounds of Delphi, and final Position Statement.
• Figure 2: Defined phases of MT intervention: (A1) primary access (femoral artery), (A2) primary access (radial artery), (B) secondary access, (C) treatment (stent shown here, but could also be an aspiration catheter) (D) removal of navigation equipment (and access closure). The "navigation phases" are considered to be (A) primary and (B) secondary access.
• Figure 3: Consensually agreed benefits and risks of robotic mechanical thrombectomy (MT), both with and without artificial intelligence (across all developmental stages of robotic MT from in silico to in vivo). Percentage agreement for each benefit and risk was taken from the round where consensus was first reached. Benefits and risks of robotic MT that did not reach consensus in any round can be found in Supplementary Table \ref{['tab:non_consensus_bens_risks']}.
• Figure 4: Consensus of which mechanical thrombectomy (MT) phases are effective for the development of robotic MT both with and without artificial intelligence (across all developmental stages of robotic MT from in silico to in vivo), for testbeds from each developmental stage. Percentage agreement for each phase was taken from the round where consensus was first reached.
• Figure 5: Evolution of testbed complexity with feature complexity. Examples of system complexity—simple in silico, simple in vitro, standard in silico, standard in vitro, standard ex vivo, complex ex vivo, and complex in vivo - are shown in Supplementary Figure \ref{['fig:simple_in_silico']} through \ref{['fig:complex_in_vivo']}, respectively.