Recovery of Motor Skills With the Use of Artificial Intelligence and Computer Vision

Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health

Status: Not yet enrolling

Conditions: Dysmetria; Stroke; Spasticity as Sequela of Stroke; Hemiparesis

Treatments: Device: AssistI patients; Device: Habilect patients

Study type: Interventional

Funder types: Other

Identifiers

NCT06183970

AssistI01

Details and patient eligibility

About

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.

Full 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.

Enrollment: 90 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

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90 participants in 3 patient groups

AssistI patients

Experimental group

Description:

Patients will receive rehabilitation training using the AsistI software package in conjunction with standard upper limb rehabilitation interventions.

Treatment:

Device: AssistI patients

Habilect patients

Active Comparator group

Description:

Patients will receive rehabilitation training using the Habilect software and hardware complex, in addition to standard rehabilitation interventions for the upper limb.

Treatment:

Device: Habilect patients

Conventional therapy patients

No Intervention group

Description:

Patients will undergo standard upper limb rehabilitation interventions without the utilization of additional methods.