Complete robotic intervention for acute epistaxis in a patient with COVID-19 pneumonia: technical considerations and device selection tips

Saber, Hamidreza; Beaman, Charles; Tateshima, Satoshi

doi:10.1136/neurintsurg-2021-018582

Cited by 4 publications

(1 citation statement)

References 9 publications

(9 reference statements)

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“…This system has performed diagnostic cerebral angiography procedures and ten carotid artery stenting procedures (Nogueira et al, 2020;Sajja et al, 2020;Weinberg et al, 2021). Furthermore, it has been recently used to perform robotassisted, neuroendovascular interventions including aneurysm embolization and epistaxis embolization (Pereira et al, 2020;Cancelliere et al, 2022;Saber et al, 2022). These systems use a controller-operator structure, where operators remotely control and navigate a robot through a patient's vasculature to the target site.…”

Section: Introductionmentioning

confidence: 99%

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

Robertshaw

Karstensen

Jackson

et al. 2023

Front. Hum. Neurosci.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Robertshaw

Karstensen

Jackson

et al. 2023

Front. Hum. Neurosci.

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The Critical Technologies of Vascular Interventional Robotic Catheterization: A Review

Ren,

Zhao,

Zhang

et al. 2023

IEEE Sensors J.

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Modeling optimal patient transport in a stroke network capable of remote telerobotic endovascular therapy

et al. 2022

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Background Telerobotic endovascular therapy (EVT) has the potential to decrease time to treatment and expand existing networks of care to more rural populations. It is currently unclear how its implementation would impact existing stroke networks. Methods Conditional probability models were generated to predict the probability of excellent outcome for patients with suspected large vessel occlusion (LVO). A baseline stroke network was created for California using existing intravenous thrombolysis (IVT) centers and comprehensive stroke centers (CSCs) capable of IVT and EVT. Optimal transport decisions and catchment areas were generated for the baseline model and three hypothetical scenarios through conversion of IVT centers at various distances from a CSC into centers capable of telerobotic EVT [i.e., hospitals ≥15 and <50 miles from a CSC were converted (Scenario 1), ≥50 and <100 miles (Scenario 2), and ≥100 miles (Scenario 3)]. Procedural times and success rates were varied systematically. Results Telerobotic EVT centers decreased median travel time for LVO patients in all three scenarios. The estimated number of robotically treated LVOs per year in Scenarios 1, 2, and 3 were 2,172, 740, and 212, respectively. Scenario 1 (15–50 miles) was the most sensitive to robotic time delay and success rate, but all three scenarios were more sensitive to decreases in procedural success rate compared to time delay. Conclusions Telerobotic EVT has the potential to improve care for stroke patients outside of major urban centers. Compared to procedural time delays in robotic EVT, a decrease in procedural success rate would not be well tolerated.

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Complete robotic intervention for acute epistaxis in a patient with COVID-19 pneumonia: technical considerations and device selection tips

Cited by 4 publications

References 9 publications

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

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

The Critical Technologies of Vascular Interventional Robotic Catheterization: A Review

Modeling optimal patient transport in a stroke network capable of remote telerobotic endovascular therapy

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