Evidence map›Paper›PMID 39628872›Full record

Article... International Symposium on Medical Robotics. International Symposium on Medical Robotics2024

Bayesian Optimization Based Preprocedural Planning For Robotic Left Atrial Appendage Occlusion.

Ran Hao, Yuttana Itsarachaiyot, M Cenk Çavuşoğlu

Abstract read
In one paragraph

Article in ... International Symposium on Medical Robotics. International Symposium on Medical Robotics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ran HaoDepartment of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH.
Yuttana ItsarachaiyotDepartment of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH.
M Cenk ÇavuşoğluDepartment of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH.

Funding

Magnetic Resonance Imaging Guided Robotic Catheter System for Left Atrial Appendage Occlusion ProceduresR01HL163991 · NHLBI · CASE WESTERN RESERVE UNIVERSITY · PI BEZERRA, HIRAM, CAVUSOGLU, M. CENK · 2022 to 2025
$3.7M
Real-Time MRI Guided Robotic Catheter System for Atrial Fibrillation AblationR01HL153034 · NHLBI · CASE WESTERN RESERVE UNIVERSITY · PI CAVUSOGLU, M. CENK, GRISWOLD, MARK · 2020 to 2023
$2.7M
NHLBI NIH HHS R01 HL153034NHLBI NIH HHS R01 HL163991
6 · The paper itself

Abstract

Left atrial appendage occlusion is a procedure to reduce the risk of thromboembolism in atrial fibrillation patients by blocking the left atrial appendage ostium using an occlusion device implanted by an intra-vascular delivery catheter. The preprocedural planning of the left atrial appendage occlusion procedure aims to identify an optimal implantation trajectory for a successful occlusion implant delivery from a structural understanding of the left atrial appendage. In this paper, a Bayesian Optimization based preprocedural planning approach is proposed for the robotic left atrial appendage occlusion procedure. The preprocedural planner efficiently samples transseptal puncture positions over the fossa ovalis and sequentially optimizes the transseptal puncture location. The iterative linear-quadratic-regulator is employed by the Bayesian Optimization planner for locally optimizing the occlusion trajectory for a given transseptal puncture location. The performance of the proposed Bayesian Optimization based preprocedural planner is evaluated in a simulation environment using a real cardiac anatomy model.

Identifiers

PMID39628872
PMCPMC11611301

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.