Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 16, 2019
Date Accepted: Mar 23, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Reliability of Assessment of Neuroimaging by Smartphone for Diffusion Weighted Imaging–Alberta Stroke Program Early Computed Tomography Scores in Acute Ischemic Stroke Patients
ABSTRACT
Background:
High-quality neuroimages can be viewed via a medical app installed on a smartphone. Although inter-device agreement between smartphone and desktop PC monitor was found to be favorable for evaluating CT images, there are no inter-device agreement data for DWI.
Objective:
Our objective is to investigate the diagnostic accuracy and interrater agreement of Diffusion-weighted Imaging–Alberta Stroke Program Early Computed Tomography Scores (DWI-ASPECTS) in acute ischemic stroke patients for neuroimages evaluated on a smartphone app and on a desktop PC monitor.
Methods:
The ischemic change in the DWI of consecutive patients with acute stroke in the MCA territory was graded by two vascular neurologists via a JOIN smartphone app and a desktop PC monitor. The vascular neurologists were blinded to all patient information. Each image was categorized as either DWI-ASPECTS ≥7 or DWI-ASPECTS <7 according to the Japan Society for Neuroendovascular Therapy. We analyzed inter-device agreement and inter-rater agreement with respect to DWI-ASPECTS. Elapsed interpretation time was compared between DWI-ASPECTS evaluated by JOIN smartphone app and that by desktop PC monitor.
Results:
We analyzed the images of 111 patients (66% male, median age, 69 years; median NIHSS score on admission, 4). Inter-device agreement regarding DWI-ASPECTS between the smartphone and the desktop PC monitor was favorable agreement (vascular neurologist 1; kappa = 0.777, p<0.001, vascular neurologist 2; kappa = 0.787, p < 0.001). Inter-rater agreement was also satisfactory for the smartphone (kappa = 0.710, p < 0.001), and the desktop PC monitor (kappa = 0.663, p < 0.001). Median elapsed interpretation time was similar between the smartphone and the desktop PC monitor (vascular neurologist 1, 1.7 vs. 1.6 min (p = 0.64); and vascular neurologist 2, 2.4 vs. 2.0 min (p = 0.14).
Conclusions:
The use of a smartphone app enables vascular neurologists to estimate DWI-ASPECTS accurately and rapidly. The JOIN medical smartphone app shows great promise in the management of acute stroke.
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