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P52 Development of a clinical tool to aid device sizing, ease of angiography and liquid embolic compatibility

2022·0 Zitationen·14th Congress of the European Society of Minimally Invasive Neurological Therapy 2022 Meeting AbstractsOpen Access
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2022

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Abstract

<h3>Introduction</h3> A great variety of neurointerventional devices now exist and practitioners may not be familiar with compatibility especially of newer devices. As procedures become more complex and guide catheters increase in size more devices can often be inserted into a single catheter lumen. Predicting in advance whether (i) they will fit and (ii) whether angiography can be performed can be non-trivial. If 3 or more devices are used in a single lumen the problem is mathematically complex (a problem termed ‘circle packing’) and compatibility cannot be calculated by summation of the diameters. If devices are unexpectedly incompatible they must be discarded, increasing cost of procedures. <h3>Aims of study</h3> Produce a model to predict device compatibility in terms of diameter, length, space for cerebral angiography, and liquid embolic usage. <h3>Methods</h3> A large database of commercially available devices (catheters, balloons, stents etc.) was compiled consisting of manufacturer provided and empirically observed data on sizing and compatibility. A computational model employing the Matoušek-Sharir-Welzl algorithm was used to predict device fitting.<sup>1,2</sup> <h3>Results</h3> The model was able to accurately predict device compatibility during procedures and was found to be a useful and easy-to-use clinical aid by neurointerventional practitioners. <h3>Conclusions</h3> A tool was developed to aid in decisions regarding device sizing, ease of angiography, and liquid embolic compatibility. The tool and source code are freely available athttps://www.neurotool.org/index.html. This is currently the only model that can accurately predict whether 3 or more devices will fit inside the single lumen of a larger device. <h3>References</h3> Matousek, Sharir,Welzl. Asubexponentialbound for Linear Programming. Algorithmica16 (1996) 498–516. Bostok, Mike.https://Observablehq.com/@D3/D3-Packenclose. [Retrieved 2 May 2022] <h3></h3> <b>Do you have any conflict of interest to declare?</b>: No

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Telemedicine and Telehealth ImplementationArtificial Intelligence in Healthcare and EducationRetinal Imaging and Analysis
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