O-046 Robotic diagnostic cerebral angiography: a multicenter experience

Beaman, Charles; Gautam, Archana; Peterson, Catherine; Kaneko, Naoki; Ponce, Lopez; Saber, Hamidreza; Khatibi, Kasra; Narsinh, Kazim; Morales, José; Kimball, David; Lipovac, Jacob; Baker, Amanda F.; Caton, M Travis; Smith, Eric R; Nour, May; Szeder, Viktor; Jahan, Reza; Colby, Geoffrey P.; Cord, Branden; Cooke, Daniel L; Tateshima, Satoshi; Duckwiler, Gary; Waldau, Ben

doi:10.1136/jnis-2023-snis.46

SNIS 20th Annual Meeting Oral Abstracts 2023

DOI: 10.1136/jnis-2023-snis.46

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O-046 Robotic diagnostic cerebral angiography: a multicenter experience

Charles Beaman¹,

Archana Gautam

Catherine Peterson

et al.

Abstract: episode of shortness of breath, respectively. All patients were discharged home the following day except 1 patient with numbness who stayed for 2 days and MRI showed ipsilateral DWI changes. The average time for the last follow-up was 5 months and mRS scores were 0 (n=25), 1 (n=16), and 2 (n=1). No patient experienced a drop in mRS at the last follow-up. Based on Time-Driven Activity-based Costing (TDABC) for ICU beds, $1,500-2,600 were saved per procedure, totaling $63,000-109,000. Conclusions This preliminar… Show more

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2024

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Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning

Robertshaw,

Karstensen,

Jackson

et al. 2024

Int J CARS

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Purpose Autonomous navigation of catheters and guidewires can enhance endovascular surgery safety and efficacy, reducing procedure times and operator radiation exposure. Integrating tele-operated robotics could widen access to time-sensitive emergency procedures like mechanical thrombectomy (MT). Reinforcement learning (RL) shows potential in endovascular navigation, yet its application encounters challenges without a reward signal. This study explores the viability of autonomous guidewire navigation in MT vasculature using inverse reinforcement learning (IRL) to leverage expert demonstrations. Methods Employing the Simulation Open Framework Architecture (SOFA), this study established a simulation-based training and evaluation environment for MT navigation. We used IRL to infer reward functions from expert behaviour when navigating a guidewire and catheter. We utilized the soft actor-critic algorithm to train models with various reward functions and compared their performance in silico. Results We demonstrated feasibility of navigation using IRL. When evaluating single- versus dual-device (i.e. guidewire versus catheter and guidewire) tracking, both methods achieved high success rates of 95% and 96%, respectively. Dual tracking, however, utilized both devices mimicking an expert. A success rate of 100% and procedure time of 22.6 s were obtained when training with a reward function obtained through ‘reward shaping’. This outperformed a dense reward function (96%, 24.9 s) and an IRL-derived reward function (48%, 59.2 s). Conclusions We have contributed to the advancement of autonomous endovascular intervention navigation, particularly MT, by effectively employing IRL based on demonstrator expertise. The results underscore the potential of using reward shaping to efficiently train models, offering a promising avenue for enhancing the accessibility and precision of MT procedures. We envisage that future research can extend our methodology to diverse anatomical structures to enhance generalizability.

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Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning

Robertshaw,

Karstensen,

Jackson

et al. 2024

Int J CARS

View full text Add to dashboard Cite

show abstract

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O-046 Robotic diagnostic cerebral angiography: a multicenter experience

Cited by 1 publication

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Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning

Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning

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