StrokeNCT06183970
Recovery of Motor Skills With the Use of Artificial Intelligence and Computer Vision
A study testing AssistI patients and Habilect patients in people with Stroke and Hemiparesis, run by Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health.
Trial summary
To investigate the impact of algorithms utilizing artificial intelligence technology and computer vision on the recovery of motor functions within the context of rehabilitation practice for patients who have experienced a cerebral stroke. Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Stroke, Hemiparesis, Spasticity as Sequela of Stroke
- What is being tested?
- AssistI patients and Habilect patients
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 90 participants
- Where
- No sites listed yet
- Study dates
- Feb 2024to Feb 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Recent hemispheric stroke (ischemic or hemorrhagic):
- Rankin scale: 3
- Within 6 months post stroke.
- Upper limb hemiparesis with strength ≤3 points proximally.
- Muscle tone rise (≤3 points) on Ashford scale.
- Complex sensitivity preserved per neuro examination
Who cannot join
- Rankin scale of 4 points and higher.
- 6 months or more after undergoing stroke.
- Structural changes in the joints of the upper extremities that limit joint mobility (contractures, ankylosis, metal structures that limit mobility).
- Severe pain syndrome in the paretic upper limb at rest or when moving, preventing exercise (7 points or more on the scale).
- Gross cognitive disorders, psychoemotional arousal, signs of hysteria, pseudobulbar syndrome (violent laughter, crying), aphasic disorders that prevent understanding of the task.
- Visual disturbances that prevent the perception of information (neglect, hemianopia, myopia, diplopia).
- Thrombosis of the veins in the upper and lower extremities without signs of recanalization, or arterial thrombosis.
- Parkinsonism and other types of tremor.
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Recovery of Motor Functions Through Assistive Motion Capture Software Using Artificial Intelligence and Computer Vision
To investigate the impact of algorithms utilizing artificial intelligence technology and computer vision on the recovery of motor functions within the context of rehabilitation practice for patients who have experienced a cerebral stroke.
Read the detailed description
Progress in artificial intelligence (AI) technologies and their practical application across various fields, notably in medicine, showcases their potential in solutions such as automated diagnostic systems, unstructured medical record recognition, natural language understanding, event analysis and prediction, information classification, automatic patient support via chatbots, and movement analysis through video. Currently, diverse AI-based software systems are being developed, designed to solve intellectual problems akin to human thinking. AI's widespread applications encompass prediction, evaluation of digital information (including unstructured data), and pattern recognition (data mining).
Amid rapid advancements in deep machine learning, particularly in image and pattern recognition, medical image analysis has gained prominence within automated diagnostic systems, particularly in radiation diagnostics. With the burgeoning field's rapid growth, curating medical datasets for AI-based diagnostic system training and validation is crucial.
AI's success in radiation diagnostics and its recognition as promising within scientific circles pave the way for video analysis and machine learning's integration into medical rehabilitation practice. Collaborating, researchers at the Federal Medical Research Center of the FMBA of Russia and MTUCI devised a plan to develop specialized algorithms based on video movement analysis and machine learning for stroke patients undergoing medical rehabilitation.
These algorithms monitor patients' movements and promptly notify them of deviations, amplitude reductions, or compensatory patterns, aiding them in correcting their movements. All session data is archived electronically, accessible to medical professionals responsible for individualized lesson plans. This enables assessment of patient progress and necessary adjustments to the home rehabilitation program.
Incorporating AI-driven video analysis and machine learning into medical rehabilitation holds great potential for enhancing patient outcomes and personalizing treatment strategies.
- Conditions
- Stroke, Hemiparesis, Spasticity as Sequela of Stroke, Dysmetria
- Being tested
- AssistI patients, Habilect patients
- Keywords
- Rehabilitation, Artificial intelligence, Computer vision, Motion capture, Assistive technology
- Medical subject headings
- Stroke, Paresis, Cerebellar Ataxia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AssistI patientsDevice
The AsistI software package rehabilitation involves tailored upper limb exercises under an individual program. The regimen consists of 10-12 sessions, each lasting 30 minutes. Patients execute 10 exercises sequentially with their unaffected and affected limbs, involving tasks like touching mouth, forehead, and trunk parts with hand's brush, and amplitude movements in upper limb joints. AsistI assesses exercise accuracy, prevents unfavorable patterns, and logs target achievement, considering speed, accuracy, and repetitions.
Habilect patientsDevice
The Habilect rehab program involves 10-12 sessions using software and hardware. Patients perform upper limb exercises for 30 minutes individually, focusing on specific movements. They repeat 10 exercises, first with the healthy limb, then the affected one. Tasks include touching mouth, forehead, and trunk, along with joint movements like shoulder flexion. Habilect assesses exercise accuracy, preventing wrong moves, and tracks progress, considering speed, accuracy, repetitions.
| Study group | Role | What they receive |
|---|---|---|
| AssistI patients | Experimental | Patients will receive rehabilitation training using the AsistI software package in conjunction with standard upper limb rehabilitation interventions. Receives: Device: AssistI patients |
| Habilect patients | Active comparator | Patients will receive rehabilitation training using the Habilect software and hardware complex, in addition to standard rehabilitation interventions for the upper limb. Receives: Device: Habilect patients |
| Conventional therapy patients | No intervention | Patients will undergo standard upper limb rehabilitation interventions without the utilization of additional methods. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Fugl-Meyer Assessment Scale for upper extremity assessment (FMA-UE)
In this study, we wiil use 36 items of the upper arm (proximal musculature, FMA-UA), 24 items of wrist and hand (distal musculature, FMA-W/H), 6 items of aspects of coordination, 12 items of aspects of sensation, 24 items of aspects of passive joint movement, 24 items of joint pain. So the maximum total score on this FMA-UE scale was 126 points.
Measured: Change from baseline at 3 weeks
Muscle strength was assessed using the MRC (Medical Research Council Weakness Scale)
MRC is a commonly used scale for assessing muscle strength from Grade 5 (normal) to Grade 0 (no visible contraction). Paresis is defined as light at compliance with strength 4 points, moderate - 3 points, pronounced - 2 points, rough - 1 point and with - 0 points.
Measured: Change from baseline at 3 weeks
The Action Research Arm Test (ARAT)
Is a 19 item observational measure used by physical therapists and other health care professionals to assess upper extremity performance (coordination, dexterity and functioning) in stroke recovery, brain injury and multiple sclerosis populations. Scores on the ARAT may range from 0-57 points, with a maximum score of 57 points indicating better performance. MCID has been suggested as 5.7 points
Measured: Change from baseline at 3 weeks
Secondary outcomes (6)
The speed of movement of the upper limb
Upper limb movement speed: Time to reach the target (sec).
Measured: Change from baseline at 3 weeks
Accuracy of performed movements
Movement accuracy: Precision in touching guided points (angles).
Measured: Change from baseline at 3 weeks
Total number of repetitions
Repetition count: Number of motor attempts for the goal.
Measured: Change from baseline at 3 weeks
The correctness of the exercises
Exercise correctness: Number of compensatory actions like shoulder elevation or torso bend.
Measured: Change from baseline at 3 weeks
The number of exercises completed
Correct repetition count: Number of attempts without compensation, e.g., shoulder or torso movements.
Measured: Change from baseline at 3 weeks
The number of exercises not completed
Incorrect repetition count: Number of attempts with compensatory actions, e.g., shoulder lift or torso bend.
Measured: Change from baseline at 3 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Dec 2023
- Study startFeb 2024 (estimated)
- Primary completionAug 2024 (estimated)
- Study completionFeb 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for StrokeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 90 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health
- Sponsor type
- University, hospital or other
- Collaborators
- Moscow Technical University of Communications and Informatics
- Responsible party
- Sponsor
- Investigators
- Michael Gorodnichev, Study chair, Moscow Technical University of Communication and Informatics (MTUCI)Galina Ivanova, Prof, Study chair, Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06183970
- Sponsor's protocol ID
- AssistI01
- Status last verified
- Aug 2023
- First submitted
- 24 Aug 2023
- Last updated
- 28 Dec 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Recovery of Motor Functions Through Assistive Motion Capture Software Using Artificial Intelligence and Computer Vision
- ClinicalTrials.gov ID
- NCT06183970
- Acronym / sponsor ID
- AssistI01
- Conditions
- Stroke, Hemiparesis, Spasticity as Sequela of Stroke, Dysmetria
- Keywords
- Rehabilitation, Artificial intelligence, Computer vision, Motion capture, Assistive technology
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 90 participants (estimated)
- Lead sponsor
- Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of HealthUniversity, hospital or other
- Collaborators
- Moscow Technical University of Communications and Informatics
- Study start
- Feb 2024 (estimated)
- Primary completion
- Aug 2024 (estimated)
- Study completion
- Feb 2025 (estimated)
- First posted
- 28 Dec 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
To investigate the impact of algorithms utilizing artificial intelligence technology and computer vision on the recovery of motor functions within the context of rehabilitation practice for patients who have experienced a cerebral stroke.
Detailed description
Progress in artificial intelligence (AI) technologies and their practical application across various fields, notably in medicine, showcases their potential in solutions such as automated diagnostic systems, unstructured medical record recognition, natural language understanding, event analysis and prediction, information classification, automatic patient support via chatbots, and movement analysis through video. Currently, diverse AI-based software systems are being developed, designed to solve intellectual problems akin to human thinking. AI's widespread applications encompass prediction, evaluation of digital information (including unstructured data), and pattern recognition (data mining).
Amid rapid advancements in deep machine learning, particularly in image and pattern recognition, medical image analysis has gained prominence within automated diagnostic systems, particularly in radiation diagnostics. With the burgeoning field's rapid growth, curating medical datasets for AI-based diagnostic system training and validation is crucial.
AI's success in radiation diagnostics and its recognition as promising within scientific circles pave the way for video analysis and machine learning's integration into medical rehabilitation practice. Collaborating, researchers at the Federal Medical Research Center of the FMBA of Russia and MTUCI devised a plan to develop specialized algorithms based on video movement analysis and machine learning for stroke patients undergoing medical rehabilitation.
These algorithms monitor patients' movements and promptly notify them of deviations, amplitude reductions, or compensatory patterns, aiding them in correcting their movements. All session data is archived electronically, accessible to medical professionals responsible for individualized lesson plans. This enables assessment of patient progress and necessary adjustments to the home rehabilitation program.
Incorporating AI-driven video analysis and machine learning into medical rehabilitation holds great potential for enhancing patient outcomes and personalizing treatment strategies.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| AssistI patients | Experimental | Device: AssistI patientsPatients will receive rehabilitation training using the AsistI software package in conjunction with standard upper limb rehabilitation interventions. |
| Habilect patients | Active comparator | Device: Habilect patientsPatients will receive rehabilitation training using the Habilect software and hardware complex, in addition to standard rehabilitation interventions for the upper limb. |
| Conventional therapy patients | No intervention | –Patients will undergo standard upper limb rehabilitation interventions without the utilization of additional methods. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- Rankin scale: 3
- Within 6 months post stroke.
- Upper limb hemiparesis with strength ≤3 points proximally.
- Muscle tone rise (≤3 points) on Ashford scale.
- Complex sensitivity preserved per neuro examination
Key exclusion criteria (8 listed)
- Rankin scale of 4 points and higher.
- 6 months or more after undergoing stroke.
- Structural changes in the joints of the upper extremities that limit joint mobility (contractures, ankylosis, metal structures that limit mobility).
- Severe pain syndrome in the paretic upper limb at rest or when moving, preventing exercise (7 points or more on the scale).
- Gross cognitive disorders, psychoemotional arousal, signs of hysteria, pseudobulbar syndrome (violent laughter, crying), aphasic disorders that prevent understanding of the task.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AssistI patientsThe AsistI software package rehabilitation involves tailored upper limb exercises under an individual program. The regimen consists of 10-12 sessions, each lasting 30 minutes. Patients execute 10 exercises sequentially with their unaffected and affected limbs, involving tasks like touching mouth, forehead, and trunk parts with hand's brush, and amplitude movements in upper limb joints. AsistI assesses exercise accuracy, prevents unfavorable patterns, and logs target achievement, considering speed, accuracy, and repetitions. | Device | AssistI patients |
| Habilect patientsThe Habilect rehab program involves 10-12 sessions using software and hardware. Patients perform upper limb exercises for 30 minutes individually, focusing on specific movements. They repeat 10 exercises, first with the healthy limb, then the affected one. Tasks include touching mouth, forehead, and trunk, along with joint movements like shoulder flexion. Habilect assesses exercise accuracy, preventing wrong moves, and tracks progress, considering speed, accuracy, repetitions. | Device | Habilect patients |
Primary outcome
Fugl-Meyer Assessment Scale for upper extremity assessment (FMA-UE)
Time frame: Change from baseline at 3 weeks
In this study, we wiil use 36 items of the upper arm (proximal musculature, FMA-UA), 24 items of wrist and hand (distal musculature, FMA-W/H), 6 items of aspects of coordination, 12 items of aspects of sensation, 24 items of aspects of passive joint movement, 24 items of joint pain. So the maximum total score on this FMA-UE scale was 126 points.
Muscle strength was assessed using the MRC (Medical Research Council Weakness Scale)
Time frame: Change from baseline at 3 weeks
MRC is a commonly used scale for assessing muscle strength from Grade 5 (normal) to Grade 0 (no visible contraction). Paresis is defined as light at compliance with strength 4 points, moderate - 3 points, pronounced - 2 points, rough - 1 point and with - 0 points.
The Action Research Arm Test (ARAT)
Time frame: Change from baseline at 3 weeks
Is a 19 item observational measure used by physical therapists and other health care professionals to assess upper extremity performance (coordination, dexterity and functioning) in stroke recovery, brain injury and multiple sclerosis populations. Scores on the ARAT may range from 0-57 points, with a maximum score of 57 points indicating better performance. MCID has been suggested as 5.7 points
6 secondary outcomes
The speed of movement of the upper limb
Time frame: Change from baseline at 3 weeks
Accuracy of performed movements
Time frame: Change from baseline at 3 weeks
Total number of repetitions
Time frame: Change from baseline at 3 weeks
The correctness of the exercises
Time frame: Change from baseline at 3 weeks
The number of exercises completed
Time frame: Change from baseline at 3 weeks
The number of exercises not completed
Time frame: Change from baseline at 3 weeks
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Dec 2023.
Related trials for Stroke
Not yet recruiting
Temporal Interference Stimulation for Upper-Limb Motor Recovery in Early Subacute Stroke
Acute Ischemic Stroke
RecruitingObservational study
ELATE Study: Evaluating Large-vessel Acute Thrombectomy in Extended Time Window
Acute Ichemic Stroke
Recruiting
Web-Based Tele-Rehabilitation Using Action Observation and Mirror Therapy for Subacute Stroke: A Randomized Controlled Trial
Stroke, Upper Extremity Paresis
Recruiting
Early Motor-Cognitive Integrative Training on Cognitive and Motor Performance in Patients With Aneurysmal Subarachnoid Hemorrhage
Subarachnoid Aneurysm Hemorrhage
Not yet recruiting
Selective Intra-Arterial Hypothermia After Endovascular Treatment for Acute Basilar Artery Occlusion
Acute Ischemic Stroke, Basilar Artery Occlusion
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Michael Gorodnichev study chair
Moscow Technical University of Communication and Informatics (MTUCI)
Galina Ivanova, Prof study chair
Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health
Questions about this trial
What is this clinical trial studying?
A study testing AssistI patients and Habilect patients in people with Stroke and Hemiparesis, run by Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 28 Dec 2023. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Danila Lobunko (+79091648192, doctorlobunko@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Fugl-Meyer Assessment Scale for upper extremity assessment (FMA-UE) (time frame: Change from baseline at 3 weeks). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Dec 2023.