Developing an Accumulative Assessment System of Upper Extremity Motor Function in Patients With Stroke Using Deep Learning

Abstract

Objective: The Fugl-Meyer assessment for upper extremity (FMA-UE) is a measure for assessing upper extremity motor function in patients with stroke. However, the considerable administration time of the assessment decreases its feasibility. This study aimed to develop an accumulative assessment system of upper extremity motor function (AAS-UE) based on the FMA-UE to improve administrative efficiency while retaining sufficient psychometric properties.

Methods: The study used secondary data from 3 previous studies having FMA-UE datasets, including 2 follow-up studies for subacute stroke individuals and 1 test-retest study for individuals with chronic stroke. The AAS-UE adopted deep learning algorithms to use patients' prior information (ie, the FMA-UE scores in previous assessments, time interval of adjacent assessments, and chronicity of stroke) to select a short and personalized item set for the following assessment items and reproduce their FMA-UE scores.

Results: Our data included a total of 682 patients after stroke. The AAS-UE administered 10 different items for each patient. The AAS-UE demonstrated good concurrent validity (r = 0.97-0.99 with the FMA-UE), high test-retest reliability (intra-class correlation coefficient = 0.96), low random measurement error (percentage of minimal detectable change = 15.6%), good group-level responsiveness (standardized response mean = 0.65-1.07), and good individual-level responsiveness (30.5%-53.2% of patients showed significant improvement). These psychometric properties were comparable to those of the FMA-UE.

Conclusion: The AAS-UE uses an innovative assessment method, which makes good use of patients' prior information to achieve administrative efficiency with good psychometric properties.

Impact: This study demonstrates a new assessment method to improve administrative efficiency while retaining psychometric properties, especially individual-level responsiveness and random measurement error, by making good use of patients' basic information and medical records.

Keywords: Deep Learning; Developing Short Forms; Stroke; Upper Extremity.