Clinical trial NCT06183970: Recovery of Motor Skills With the Use of Artificial Intelligence and Computer Vision
Official title: Recovery of Motor Functions Through Assistive Motion Capture Software Using Artificial Intelligence and Computer Vision
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Stroke
Hemiparesis
Spasticity as Sequela of Stroke
Dysmetria
Keywords: Rehabilitation
Artificial intelligence
Computer vision
Motion capture
Assistive technology
Lead sponsor: Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health
Sponsor class: OTHER
Collaborators: name: Moscow Technical University of Communications and Informatics
First posted: 2023-12-28
Enrollment: 90
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: TREATMENT

Brief 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.

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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Fugl-Meyer Assessment Scale for upper extremity assessment (FMA-UE)
timeFrame: Change from baseline at 3 weeks
measure: Muscle strength was assessed using the MRC (Medical Research Council Weakness Scale)
timeFrame: Change from baseline at 3 weeks
measure: The Action Research Arm Test (ARAT)
timeFrame: Change from baseline at 3 weeks
Secondary outcomes:
measure: The speed of movement of the upper limb
timeFrame: Change from baseline at 3 weeks
measure: Accuracy of performed movements
timeFrame: Change from baseline at 3 weeks
measure: Total number of repetitions
timeFrame: Change from baseline at 3 weeks
measure: The correctness of the exercises
timeFrame: Change from baseline at 3 weeks
measure: The number of exercises completed
timeFrame: Change from baseline at 3 weeks
measure: The number of exercises not completed
timeFrame: Change from baseline at 3 weeks
Other outcomes:
Locations:
Study officials:
affiliation: Moscow Technical University of Communication and Informatics (MTUCI)
name: Michael Gorodnichev
role: STUDY_CHAIR
affiliation: Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health
name: Galina Ivanova, Prof
role: STUDY_CHAIR
References: