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Highland Instruments Publishes Study on Predicting Stroke Recovery with AI-Powered Suite
EntSun News/11068985
Integrated Motion Analysis Suite (IMAS), an AI-Powered Platform, Has Potential to Enable Objective Symptom Assessment and Personalized Treatment Plans for Stroke Patients
CAMBRIDGE, Mass. - EntSun -- Highland Instruments today announced the publication of a peer-reviewed study in Information Sciences demonstrating how our Integrated Motion Analysis Suite (IMAS) can predict recovery after stroke using a combination of wearable sensors, motion tracking, and clinical data.
In a study of 28 stroke patients, IMAS was able to:
"IMAS helps translate complex clinical data into insights that can help clinicians predict stroke patients' recoveries and enable personalized treatment plans," said Laura Dipietro, PhD, Chief Technology Officer at Highland Instruments.
"With AI-driven analytics providing neurologists with actionable insights about patients' symptoms, IMAS has the potential to catalyze precision treatment for neurological conditions beyond stroke, including movement disorders," added Tim Wagner, PhD, Chief Scientific Officer.
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The paper - "A multimodal machine learning approach to predict Fugl-Meyer scores and motor recovery potential in stroke rehabilitation" - is available online in Information Sciences (https://www.sciencedirect.com/science/article/pii/S0020025525006978?via%3Dihub).
This works builds on and extends Highland's prior work on IMAS for Unified Parkinson's Disease Rating Scale prediction which was published in the Journal of Big Data "Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease" (https://journalofbigdata.springeropen.com/artic...).
About Highland Instruments
Highland Instruments develops noninvasive brain stimulation and data integration technologies to address unmet needs in addiction, chronic pain, movement disorders, and neurological diseases. Learn more at www.highlandinstruments.com.
In a study of 28 stroke patients, IMAS was able to:
- Accurately predict motor function and recovery, matching clinical gold standards.
- Provide more detailed, objective assessments than traditional rating scales.
- Function in real-world settings with quick setup and tolerance for incomplete data.
"IMAS helps translate complex clinical data into insights that can help clinicians predict stroke patients' recoveries and enable personalized treatment plans," said Laura Dipietro, PhD, Chief Technology Officer at Highland Instruments.
"With AI-driven analytics providing neurologists with actionable insights about patients' symptoms, IMAS has the potential to catalyze precision treatment for neurological conditions beyond stroke, including movement disorders," added Tim Wagner, PhD, Chief Scientific Officer.
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The paper - "A multimodal machine learning approach to predict Fugl-Meyer scores and motor recovery potential in stroke rehabilitation" - is available online in Information Sciences (https://www.sciencedirect.com/science/article/pii/S0020025525006978?via%3Dihub).
This works builds on and extends Highland's prior work on IMAS for Unified Parkinson's Disease Rating Scale prediction which was published in the Journal of Big Data "Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease" (https://journalofbigdata.springeropen.com/artic...).
About Highland Instruments
Highland Instruments develops noninvasive brain stimulation and data integration technologies to address unmet needs in addiction, chronic pain, movement disorders, and neurological diseases. Learn more at www.highlandinstruments.com.
Source: Highland Instruments
Filed Under: Health
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