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

Stroke Clinical Trial

Official title: Recovery of Motor Functions Through Assistive Motion Capture Software Using Artificial Intelligence and Computer Vision

Status Not yet recruiting

Clinical 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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Hemiparesis
• Paresis
• Spasticity as Sequela of Stroke
• Stroke

• NCT number: NCT06183970
• Study type: Interventional
• Source: Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health
• Contact: Danila Lobunko
• Phone: +79091648192
• Email: doctorlobunko@gmail.com
• Status: Not yet recruiting
• Phase: N/A
• Start date: February 2024
• Completion date: February 2025