Advancements in Autonomous Navigation: Utilizing Transformer Models for Catheter Tip Localization in Fluoroscopy
By
Harry Robertshaw
Yanghe Hao
Weiyuan Deng
Benjamin Jackson
S. M. Hadi Sadati
Nikola Fischer
Tom Vercauteren
Alejandro Granados
Thomas C. Booth
April 27, 2026
Clinical Scorecard: Advancements in Autonomous Navigation: Utilizing Transformer Models for Catheter Tip Localization in Fluoroscopy
At a Glance
Category Detail
Condition Stroke due to large vessel occlusion (ensure alignment with latest guidelines) Key Mechanisms Deep learning-based catheter and guidewire segmentation and tip tracking Target Population Patients eligible for mechanical thrombectomy Care Setting Endovascular intervention
Key Highlights
Mechanical thrombectomy efficacy declines beyond 7.3 hours of stroke onset AI-based autonomous navigation aims to improve catheter tip localization (clarify significance of CathAction dataset) CathAction dataset provides extensive annotated frames for training Modern segmentation architectures integrated into tracking pipelines show improved performance Real-time tracking validated across in vitro and in vivo clinical data Guideline-Based Recommendations
Diagnosis
Assess eligibility for mechanical thrombectomy in stroke patients Management
Utilize AI-driven navigation systems to enhance catheter tip localization Monitoring & Follow-up
Evaluate tracking performance in real-time clinical settings (include specific metrics) Risks
Consider risks of vessel perforations, dissections, and distal embolization Patient & Prescribing Data
Patients with large vessel occlusion strokes
AI-enhanced navigation may increase treatment accessibility and efficacy (include contraindications)
Clinical Best Practices
Implement deep learning models for improved device tracking Use the CathAction dataset for training and evaluation of segmentation models Ensure real-time tracking capabilities in clinical environments (add ongoing training recommendation) References
