Artificial intelligence in the autonomous navigation of endovascular interventions: a systematic review

Frontiers in Human Neuroscience

Human Neuroscience

Lennart Karstensen¹  Kawal Rhode²  Harry Robertshaw²  Hadi Sadati²  Sebastien Ourselin²  Benjamin Jackson²  Alejandro Granados²  Thomas C. Booth³ 

[1] Fraunhofer IPA, Mannheim, Germany;AIBE, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany;School of Biomedical Engineering & Imaging Sciences, Kings College London, London, United Kingdom;School of Biomedical Engineering & Imaging Sciences, Kings College London, London, United Kingdom;Department of Neuroradiology, Kings College Hospital, London, United Kingdom;

关键词: artificial intelligence;    machine learning;    endovascular intervention;    autonomy;    navigation;   

DOI  :  10.3389/fnhum.2023.1239374

 received in 2023-06-15, accepted in 2023-07-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundAutonomous navigation of catheters and guidewires in endovascular interventional surgery can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment.ObjectiveTo determine from recent literature, through a systematic review, the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous navigation of catheters and guidewires for endovascular interventions.MethodsPubMed and IEEEXplore databases were searched to identify reports of AI applied to autonomous navigation methods in endovascular interventional surgery. Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). PROSPERO: CRD42023392259.ResultsFour hundred and sixty-two studies fulfilled the search criteria, of which 14 studies were included for analysis. Reinforcement learning (RL) (9/14, 64%) and learning from expert demonstration (7/14, 50%) were used as data-driven models for autonomous navigation. These studies evaluated models on physical phantoms (10/14, 71%) and in-silico (4/14, 29%) models. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%), while non-anatomical vessel platforms “idealized” for simple navigation were used in three studies (3/14, 21%), and the porcine liver venous system in one study. We observed that risk of bias and poor generalizability were present across studies. No procedures were performed on patients in any of the studies reviewed. Moreover, all studies were limited due to the lack of patient selection criteria, reference standards, and reproducibility, which resulted in a low level of evidence for clinical translation.ConclusionDespite the potential benefits of AI applied to autonomous navigation of endovascular interventions, the field is in an experimental proof-of-concept stage, with a technology readiness level of 3. We highlight that reference standards with well-identified performance metrics are crucial to allow for comparisons of data-driven algorithms proposed in the years to come.Systematic review registrationidentifier: CRD42023392259.

【 授权许可】

Unknown   
Copyright © 2023 Robertshaw, Karstensen, Jackson, Sadati, Rhode, Ourselin, Granados and Booth.