Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Feb 4, 2019
Open Peer Review Period: Feb 5, 2019 - Mar 26, 2019
Date Accepted: May 12, 2019
(closed for review but you can still tweet)
Feasibility of a Real-Time Clinical Augmented Reality and Artificial Intelligence Framework for Pain Detection and Localization from the Brain
ABSTRACT
Background:
For many years clinicians have been seeking for objective pain assessment solutions via neuroimaging techniques, focusing on the brain to detect human pain. Unfortunately, most of those techniques are not applicable in the clinical environment or lack accuracy.
Objective:
In this study, we tested the feasibility of a mobile neuroimaging-based clinical augmented reality (AR) and artificial intelligence (AI) framework, CLARAi, for objective pain detection and also localization direct from the patient’s brain in real-time.
Methods:
Clinical dental pain was triggered in 21 patients by hypersensitive tooth stimulation with 20 consecutive descending cold stimulations (32° to 0 °C). We used a portable optical neuroimaging technology, functional near-infrared spectroscopy (fNIRS), to gauge their cortical activity during evoked acute clinical pain. The neuroimaging data were then transmitted in real-time to an AR device, HoloLens, allowing visualization of the ongoing cortical activity on a 3D brain template virtually plotted on the patients’ head during clinical consult. In addition, the data was decoded using a neural network (NN) based AI algorithm to classify hemodynamic response data into pain and no-pain brain states in real-time. We tested the performance of three networks (NN with 7 layers, 5 layers, and 3 layers) upon reorganized data features in simulated real-time environment.
Results:
The 3-layer NN network achieved an optimal classification accuracy at 77.59%, with positive likelihood ratio at 2.26. We further explored a three-class prediction of left/right side pain, and no-pain states which achieved classification accuracy at 71.5%.
Conclusions:
Additional studies are needed to optimize and validate our prototype CLARAi framework for other pains and neurologic disorders. However, we presented an innovative and feasible neuroimaging-based AR/AI concept that can potentially transform human brain into an objective target to visualize, and precisely measure and localize pain in real-time where it is most needed: in the doctor’s office.
Citation
Per the author's request the PDF is not available.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.