NCT06828679

Using AI Systems to Optimize the Clinical Outcome of Stroke Patients

Recruiting now NA Last updated 22 December 2025

What this trial tests

NA trial testing Acupuncture in Acute Stroke Intervention in 400 participants. Currently enrolling.

Timeline

1 July 2025

Primary endpoint
31 December 2027

30 June 2028

Quick facts

Lead sponsor	Chinese University of Hong Kong
Phase	NA
Status	Recruiting now
Study type	INTERVENTIONAL
Allocation	randomized
Design	parallel
Masking	double
Primary purpose	supportive care
Enrollment	400
Start date	1 July 2025
Primary completion	31 December 2027
Estimated completion	30 June 2028
Sites	1 location across Hong Kong

Drugs / interventions tested

• Acupuncture — full drug profile →
• control group — full drug profile →
• Robotic Training
• Neuromuscular Electrical Stimulation Group

Conditions studied

• Acute Stroke Intervention — all drugs for Acute Stroke Intervention →
• Acute Stroke — all drugs for Acute Stroke →
• Acute Ischemic Stroke — all drugs for Acute Ischemic Stroke →

Sponsor

Chinese University of Hong Kong

Who can join

Adults 65 to 80, any sex, with Acute Stroke Intervention or Acute Stroke. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

This project addresses the imminent challenge of providing adequate motor rehabilitation to a growing number of stroke survivors amidst the ageing population, decreasing age of stroke, and shortage of physical/occupational therapists in Hong Kong through AI and precision rehabilitation. To reduce the socioeconomic burden from the stroke survivors' loss of independence and their care (\>HK$15 billion/year), the efficacy of rehabilitation and efficiency of its delivery must be improved. These goals can be achieved by prescribing them with individually tailored rehabilitations predicted to yield maximal functional return. Defining a predictive model for such personalization remains challenging given the immense heterogeneity of stroke. The investigators aim to build an explainable AI system that predicts a subject's recovery potential and the treatment option that may realize this potential based on multi-modal pre-rehab assessments. Data from clinical, neuroimaging, neurophysiological, and multi-omic evaluations will be collected from stroke survivors (N≥400) before they undergo upper limb rehab with usual care, neuromuscular stimulation, robotic training, or acupuncture. Machine learning-extracted data features will be used to train decision-tree and neural-network AI algorithms for robust predictions. As soon as the model is validated, the investigators will deploy it to implement a personalized rehab program in the community. Our model's ability to predict the optimal intervention from a wide spectrum of input modalities distinguishes ours from previous less-than-accurate models. Our interdisciplinary team of 13 PIs with expertise in neurology, PT/OT, acupuncture, electrical/biomed. engineering, robotics, neuroscience, neuroimaging, multi-omics, data science, and clinical trial management will put us in a world-unique position to execute this project successfully and generate opportunities of interdisciplinary education. In the long run, our prediction system will accelerate marketization of new rehab strategies by facilitating their clinical-trial evaluations in more targeted subjects, thereby leading Hong Kong to be a future global hub of innovative rehabilitation.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.