An interventional study of AssistI patients and Habilect patients in Stroke, Hemiparesis and Spasticity as Sequela of Stroke, sponsored by Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health. Not yet recruiting. Open to participants aged 18 Years to 80 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2023-12-28.
Sponsored by Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health · Not applicable, Interventional, and Treatment
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.
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.
8,255 studies on the registry are indexed under Stroke; 2,056 are open to participants now.
This study's planned enrollment of 90 is above the median of 49 across 6,024 interventional studies indexed under Stroke.
Browse Stroke studies →Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health is the lead sponsor of 8 studies on the registry; 4 are open to participants now.
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Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Recent hemispheric stroke (ischemic or hemorrhagic):
Exclusion Criteria:
Patients will receive rehabilitation training using the AsistI software package in conjunction with standard upper limb rehabilitation interventions.
Device: AssistI patients
Patients will receive rehabilitation training using the Habilect software and hardware complex, in addition to standard rehabilitation interventions for the upper limb.
Device: Habilect patients
Patients will undergo standard upper limb rehabilitation interventions without the utilization of additional methods.
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.
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.
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.
Time frame: 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.
Time frame: 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
Time frame: Change from baseline at 3 weeks
The speed of movement of the upper limb
Upper limb movement speed: Time to reach the target (sec).
Time frame: Change from baseline at 3 weeks
Accuracy of performed movements
Movement accuracy: Precision in touching guided points (angles).
Time frame: Change from baseline at 3 weeks
Total number of repetitions
Repetition count: Number of motor attempts for the goal.
Time frame: Change from baseline at 3 weeks
The correctness of the exercises
Exercise correctness: Number of compensatory actions like shoulder elevation or torso bend.
Time frame: 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.
Time frame: 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.
Time frame: Change from baseline at 3 weeks
No study locations are listed for this record.
Start is estimated for Feb 2024; primary completion for Aug 2024, 6 months later.
Of 3,108 non-industry interventional studies with no phase under Stroke, half reached primary completion within 23 months of starting (the median), counting those still running; a quarter within 10 and three quarters within 46. This study's planned 6 months is below that.
Counting every sponsor, half of 3,252 studies reached it within 23 months; a quarter within 10 and three quarters within 46.
How these figures are worked out, and which studies are left out of them.
This study is not yet recruiting, as verified in Aug 2023. You cannot join it, but the record below documents what was studied.
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Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health