Recovery of Motor Functions Through Assistive Motion Capture Software Using Artificial Intelligence and Computer Vision
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 90
- 主要终点
- The Action Research Arm Test (ARAT)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Treatment
- 盲法
- Double (Participant, Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- •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.
结局指标
主要结局
The Action Research Arm Test (ARAT)
时间窗: 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
Muscle strength was assessed using the MRC (Medical Research Council Weakness Scale)
时间窗: 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.
Fugl-Meyer Assessment Scale for upper extremity assessment (FMA-UE)
时间窗: 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.
次要结局
- The number of exercises completed(Change from baseline at 3 weeks)
- The speed of movement of the upper limb(Change from baseline at 3 weeks)
- Accuracy of performed movements(Change from baseline at 3 weeks)
- Total number of repetitions(Change from baseline at 3 weeks)
- The correctness of the exercises(Change from baseline at 3 weeks)
- The number of exercises not completed(Change from baseline at 3 weeks)