PT - JOURNAL ARTICLE AU - Dee Zhen Lim AU - Melissa Yeo AU - Ariel Dahan AU - Bahman Tahayori AU - Hong Kuan Kok AU - Mohammad Abbasi-Rad AU - Julian Maingard AU - Numan Kutaiba AU - Jeremy Russell AU - Vincent Thijs AU - Ashu Jhamb AU - Ronil V Chandra AU - Mark Brooks AU - Christen Barras AU - Hamed Asadi TI - Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study AID - 10.1136/neurintsurg-2021-017858 DP - 2021 Aug 22 TA - Journal of NeuroInterventional Surgery PG - neurintsurg-2021-017858 4099 - http://jnis.bmj.com/content/early/2021/08/22/neurintsurg-2021-017858.short 4100 - http://jnis.bmj.com/content/early/2021/08/22/neurintsurg-2021-017858.full AB - 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.Data are available upon reasonable request.