Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study. (23rd August 2021)

• Record Type: Journal Article
• Authors: Lim, Dee Zhen; Yeo, Melissa; Dahan, Ariel; Tahayori, Bahman; Kok, Hong Kuan; Abbasi-Rad, Mohammad; Maingard, Julian; Kutaiba, Numan; Russell, Jeremy; Thijs, Vincent; Jhamb, Ashu; Chandra, Ronil V; Brooks, Mark; Barras, Christen; Asadi, Hamed
• Is Part Of: Journal of neurointerventional surgery. Volume 14:Number 8(2022)
• Page Start: 799
• Page End: 803
• Publication Date: 2021-08-23
• Subjects: stroke -- technology; Nervous system -- Surgery -- Periodicals; Cerebrovascular disease -- Surgery -- Periodicals; 617.48
• DOI: 10.1136/neurintsurg-2021-017858
• Languages: English
• ISSNs: 1759-8478

Abstract

Abstract : Background: Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention. Methods: We conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24-hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43x×1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction. Results: ML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested. Conclusions: ML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls.