부산대학교 도서관

Background:We developed an automated smart phone application for detection of acute stroke using machine learning (ML) algorithms for recognition of facial asymmetry, arm weakness, and speech changes.Methods:We analysed prospectively collected data from patients admitted to 4 major metropolitan stroke centers with confirmed diagnosis of acute stroke. Speech and facial data were captured via video recording and arm data was captured via device sensors. A. Face. This module extracts 68 facial landmark points that are passed through a dimensionality reduction step and an asymmetry classifier. We implemented and compared 26 classification methods with neurologists' clinical impression and determined Quadratic Discriminative Analysis as the best one in terms of accuracy and interpretability. B. Arm. Using data extracted from 3D accelerometer, gyroscope, and magnetometer , we designed a grasp agnostic classifier based on AdaBoost to process motion trajectories and detect arm weakness.C. Speech. We developed an algorithm based on frequency analysis and Mel Frequency Cepstral Coefficients (MFCC) to detect abnormal/slurred speech. All tests were conducted within 72 hours of admission. Each of the three ML outputs was correlated with neurologists’ clinical impression.Results:Among the 269 analysed patients, 41% were female, the median age was 71, % had hemorrhagic and % had ischemic stroke. Final analyses of 18311 facial images revealed 99.42% sensitivity, 93.67% specificity, and 97.11% accuracy in detection of facial asymmetry. The results for 43 arm trajectories revealed 71.42% sensitivity, 72.41% specificity, and 72.09% accuracy in detection of arm weakness. Preliminary analysis of MFCC algorithms confirmed adequate features for abnormal speech detectionConclusions:Our preliminary results confirm that smartphone enabled ML-algorithms can reliably identify acute stroke features with accuracy comparable to neurologists’ clinical impression.