Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke

Abstract

We investigated the potential of machine learning techniques, at an early stage after stroke, to predict the need for ankle–foot orthosis (AFO) in stroke patients. We retrospectively recruited 474 consecutive stroke patients. The need for AFO during ambulation (output variable) was classified according to the Medical Research Council (MRC) score for the ankle dorsiflexor of the affected limb. Patients with an MRC score of < 3 for the ankle dorsiflexor of the affected side were considered to require AFO, while those with scores ≥ 3 were considered not to require AFO. The following demographic and clinical data collected when patients were transferred to the rehabilitation unit (16.20 ± 6.02 days) and 6 months after stroke onset were used as input data: age, sex, type of stroke (ischemic/hemorrhagic), motor evoked potential data on the tibialis anterior muscle of the affected side, modified Brunnstrom classification, functional ambulation category, MRC score for muscle strength for shoulder abduction, elbow flexion, finger flexion, finger extension, hip flexion, knee extension, and ankle dorsiflexion of the affected side. For the deep neural network model, the area under the curve (AUC) was 0.887. For the random forest and logistic regression models, the AUC was 0.855 and 0.845, respectively. Our findings demonstrate that machine learning algorithms, particularly the deep neural network, are useful for predicting the need for AFO in stroke patients during the recovery phase.

Event-Triggered Adaptive Hybrid Torque-Position Control (ET-AHTPC) for Robot-Assisted Ankle Rehabilitation

“…During the actual rehabilitation sessions, the patient's motor ability and assistance needs are different. Brunnstrom scale is a common clinical assessment tool for the patient's motor ability, in which the stage III/IV/V are appropriate to facilitate ankle rehabilitation [23]. A 3-Degrees-of-Freedom (DOF) ankle rehabilitation robot driven by pneumatic muscles has been developed, as shown in Fig.…”

A Survey of Human Gait-Based Artificial Intelligence Applications

“…The domain of smart gait devices and environments is exciting, brave, creative, extensive, and ever-growing (See Table 12 ). SG devices include wearable shoes ( Zou et al, 2020 ), socks ( Zhang et al, 2020f ), kneepads and anklets ( Totaro et al, 2017 ), insoles ( Low et al, 2020 ), as well as devices attached to the body, such as smartphones ( Poniszewska-Maranda et al, 2019 ), smartwatches ( San-Segundo et al, 2018 ), ( Sigcha et al, 2021 ), etc., implantable medical devices such as ActiGait ( Sturma et al, 2019 ), wearable robotics ( Shi et al, 2019 ) such as prosthetics ( Gao et al, 2020 ) orthotics ( Zhang et al, 2020e ), ( Choo et al, 2021 ), assistive devices such as smart walkers ( Jimenez et al, 2018 ), and environmental devices such as smart tiles ( Daher et al, 2017 ). SG devices use gait data to facilitate health monitoring, including passive mental health assessment ( Rabbi et al, 2011 ) and transfer data to control devices for health, sports, security, and entertainment applications.…”

Deep Learning Algorithm Trained on Brain Magnetic Resonance Images and Clinical Data to Predict Motor Outcomes of Patients With Corona Radiata Infarct

“…Deep learning is a recent artificial intelligence technique in which a system learns patterns and rules from the available data ( Lee et al, 2020 ; Choo et al, 2021 ; Sarker, 2021 ). Thus, it has been increasingly applied in the clinical field.…”

“…To develop algorithms including those for deep learning, most existing studies have focused on either clinical or imaging data ( Choo et al, 2021 ; Kim et al, 2021a , b ). However, developing algorithms using clinical data requires a large number of variables, and each hospital collects different types of clinical data.…”