Stroke Clinical Trial
Official title:
Development and Validation of a Machine Learning-based Model to Predict a High-risk Group for Falls Using Multi-sensor Signals in Stroke Patients
The study assesses a machine learning model developed to predict fall risk among stroke patients using multi-sensor signals. This prospective, multicenter, open-label, sponsor-initiated confirmatory trial aims to validate the safety and efficacy of the model which utilizes electromyography (EMG) signals to categorize patients into high-risk or low-risk fall categories. The innovative approach hopes to offer a predictive tool that enhances preventative strategies in clinical settings, potentially reducing fall-related injuries in stroke survivors.
Status | Not yet recruiting |
Enrollment | 90 |
Est. completion date | April 28, 2026 |
Est. primary completion date | April 28, 2025 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 19 Years and older |
Eligibility | Stroke Participants Inclusion Criteria: - 19 years and older - the onset of the stroke is less than 3months ago - Lower extremity weakness due to stroke (MMT =< 4 grade) - Cognitive ability to follow commands Exclusion Criteria: - stroke recurrence - other neurological abnormalities (e.g. parkinson's disease). - severely impaired cognition - serious and complex medical conditions(e.g. active cancer) - cardiac pacemaker or other implanted electronic system Health Participants Inclusion Criteria: - 19 years and older - Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects Exclusion Criteria: - other neurological abnormalities (e.g. parkinson's disease). - severely impaired cognition - serious and complex medical conditions(e.g. active cancer) - cardiac pacemaker or other implanted electronic system |
Country | Name | City | State |
---|---|---|---|
n/a |
Lead Sponsor | Collaborator |
---|---|
Seoul National University Hospital | Ministry of Trade, Industry & Energy, Republic of Korea |
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Other | Area Under the Receiver Operating Characteristic Curve | This is a performance measurement for classification problems at various threshold settings. ROC is a probability curve, and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. | At the time of the single visit | |
Other | Matthews Correlation Coefficient | The MCC is used in machine learning as a measure of the quality of binary classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. | At the time of the single visit | |
Primary | Sensitivity of the Machine Learning Model | The primary outcome measure is the sensitivity of the machine learning model, which refers to its ability to correctly identify patients who are at high risk of falls. Sensitivity is defined as the proportion of actual positives that are correctly identified. | At the time of the single visit | |
Secondary | Specificity of the Machine Learning Model | Specificity measures the proportion of actual negatives that are correctly identified. | At the time of the single visit |
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