StrokeNCT07544927
Clinical Validation of an Artificial Intelligence-Based Scoring System for the Modified Rankin Scale (mRS) in Patients With Stroke
An observational study of Stroke, run by Xuanwu Hospital, Beijing.
Trial summary
This study aims to validate the clinical performance of an artificial intelligence (AI)-based automatic scoring system for the Modified Rankin Scale (mRS). The core comparison is the consistency and accuracy between the AI-generated scores and standardized manual mRS follow-up assessments performed by trained… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Stroke
- How many people
- 490 participants
- Where
- No sites listed yet
- Study dates
- 10 Apr 2026to 31 Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Consecutive stroke patients from multiple centers who are ≥18 years old, with confirmed stroke, and able to complete the 1-week follow-up will be enrolled. Patients with severe non-stroke neurological diseases, inability to communicate, or loss to follow-up will be excluded.
Who may be able to join
- Age ≥ 18 years, male or female.
- Clinically diagnosed with stroke, and confirmed by cranial CT/MRI to have stroke.
- Clinically stable, with basic communication ability at discharge or outpatient visit. The patient or a fixed family caregiver is able to cooperate with telephone follow-up at 1 week after discharge or outpatient visit.
- Signed informed consent by the patient or their legally authorized representative.
Who cannot join
- Neurological deficits caused by non-stroke etiologies (e.g., brain tumor, traumatic brain injury, encephalitis).
- Presence of severe disturbance of consciousness, severe cognitive impairment, psychiatric disorders, or global aphasia at discharge/outpatient visit, preventing effective communication; neither the patient nor family can cooperate with follow-up or assessment.
- Combined with severe multi-organ failure (e.g., cardiac, hepatic, renal, respiratory), with an expected survival of less than 1 month, making completion of the 1-week follow-up impossible.
- Long-term bedridden without a fixed caregiver, with no confirmed contact for follow-up, or refusal to participate in telephone follow-up and mRS assessment.
- Incomplete clinical data, preventing baseline data collection.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
This study aims to validate the clinical performance of an artificial intelligence (AI)-based automatic scoring system for the Modified Rankin Scale (mRS). The core comparison is the consistency and accuracy between the AI-generated scores and standardized manual mRS follow-up assessments performed by trained professionals. The goal is to provide a convenient, efficient, and objective tool for stroke prognosis assessment, reduce the subjective variability of manual scoring, and optimize the stroke follow-up workflow.
Read the detailed description
This is a prospective, multicenter, observational study designed to validate the diagnostic performance of an AI-based automated scoring system for the Modified Rankin Scale (mRS) in patients with stroke. The primary objective is to evaluate the agreement between AI-generated mRS scores and standardized manual assessments conducted by trained clinicians. Secondary endpoints include the system's sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in classifying functional outcomes.
- Conditions
- Stroke
- Keywords
- Stroke, Modified Rankin Scale, Artificial Intelligence
- Medical subject headings
- Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| AI-first interview group | Participants first receive telephone assessment by AI, followed by telephone assessment by human assessors. | |
| Human-first group | Participants first receive telephone assessment by human assessors, followed by telephone assessment by AI. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Agreement Between Artificial Intelligence (AI)-Based and Manual Modified Rankin Scale (mRS) Assessments
The weighted kappa coefficient quantifies the level of agreement between the Artificial Intelligence (AI)-generated Modified Rankin Scale (mRS) scores and the standardized manual mRS assessments performed by trained clinicians
Measured: 7 days post-discharge or post-outpatient visit, ± 2 days
Secondary outcomes (3)
Agreement Between AI-based and Manual Assessments of Dichotomized Modified Rankin Scale (mRS)
The simple kappa coefficient quantifies the level of agreement between the Artificial Intelligence (AI)-generated dichotomized Modified Rankin Scale (mRS) scores (0-2 vs. 3-6) and the standardized manual mRS assessments performed by trained clinicians
Measured: 7 days post-discharge or post-outpatient visit, ± 2 days
Bland-Altman Limits of Agreement Between AI and Manual Modified Rankin Scale (mRS) Scores
The Bland-Altman limits of agreement analysis evaluates the consistency between the Artificial Intelligence (AI)-generated and manually assessed Modified Rankin Scale (mRS) scores. The difference between manual and AI scores will be plotted on the y-axis against their mean on the x-axis, with limits of agreement (mean difference ± 1.96 × standard deviation) calculated. The analysis aims to visually assess how agreement varies across the range of mRS scores and identify any proportional bias, such as greater disagreement in patients with severe disability.
Measured: 7 days post-discharge or post-outpatient visit, ± 2 days
Diagnostic Performance of AI-Based vs. Manual Modified Rankin Scale (mRS) Dichotomization
The diagnostic performance analysis evaluates the ability of the Artificial Intelligence (AI)-based Modified Rankin Scale (mRS) scoring system to classify functional outcomes, using manual assessment as the reference standard. A 2×2 contingency table will be constructed for the dichotomized mRS categories (good outcome: 0-2 vs. poor outcome: 3-6). The analysis will calculate sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and Youden's index. A receiver operating characteristic (ROC) curve will be plotted, and the area under the curve (AUC) will be computed.
Measured: 7 days post-discharge or post-outpatient visit, ± 2 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Apr 2026
- Study start10 Apr 2026 (estimated)
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for StrokeStudies of the same condition, open and under way.
- More trials run by Xuanwu Hospital, BeijingThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 490 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xuanwu Hospital, Beijing
- Sponsor type
- University, hospital or other
- Collaborators
- Capital Medical University, People's Hospital of Beijing Daxing District, Beijing Tiantan Hospital, The First Hospital of Fangshan District,Beijing
- Responsible party
- Principal investigator, qingfeng ma, Xuanwu Hospital, Beijing
- Investigators
- Qingfeng Ma, MD, Study chair, Xuanwu Hospital, Beijing
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07544927
- Sponsor's protocol ID
- qingfeng ma AI mRS
- Status last verified
- Apr 2026
- First submitted
- 8 Apr 2026
- Last updated
- 22 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Due to the protection of participant privacy and institutional review board requirements, individual participant data (IPD) will not be shared publicly.
Study at a glance
From the registry record, last updated by the sponsor on 22 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Validation of an Artificial Intelligence-Based Scoring System for the Modified Rankin Scale (mRS) in Patients With Stroke
- ClinicalTrials.gov ID
- NCT07544927
- Acronym / sponsor ID
- qingfeng ma AI mRS
- Conditions
- Stroke
- Keywords
- Stroke, Modified Rankin Scale, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 490 participants (estimated)
- Lead sponsor
- Xuanwu Hospital, BeijingUniversity, hospital or other
- Collaborators
- Capital Medical University, People's Hospital of Beijing Daxing District, Beijing Tiantan Hospital, The First Hospital of Fangshan District,Beijing
- Study start
- 10 Apr 2026 (estimated)
- Primary completion
- 31 Dec 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 22 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to validate the clinical performance of an artificial intelligence (AI)-based automatic scoring system for the Modified Rankin Scale (mRS). The core comparison is the consistency and accuracy between the AI-generated scores and standardized manual mRS follow-up assessments performed by trained professionals. The goal is to provide a convenient, efficient, and objective tool for stroke prognosis assessment, reduce the subjective variability of manual scoring, and optimize the stroke follow-up workflow.
Detailed description
This is a prospective, multicenter, observational study designed to validate the diagnostic performance of an AI-based automated scoring system for the Modified Rankin Scale (mRS) in patients with stroke. The primary objective is to evaluate the agreement between AI-generated mRS scores and standardized manual assessments conducted by trained clinicians. Secondary endpoints include the system's sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in classifying functional outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-first interview group | – | –Participants first receive telephone assessment by AI, followed by telephone assessment by human assessors. |
| Human-first group | – | –Participants first receive telephone assessment by human assessors, followed by telephone assessment by AI. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Consecutive stroke patients from multiple centers who are ≥18 years old, with confirmed stroke, and able to complete the 1-week follow-up will be enrolled. Patients with severe non-stroke neurological diseases, inability to communicate, or loss to follow-up will be excluded.
Key inclusion criteria (4 listed)
- Age ≥ 18 years, male or female.
- Clinically diagnosed with stroke, and confirmed by cranial CT/MRI to have stroke.
- Clinically stable, with basic communication ability at discharge or outpatient visit. The patient or a fixed family caregiver is able to cooperate with telephone follow-up at 1 week after discharge or outpatient visit.
- Signed informed consent by the patient or their legally authorized representative.
Key exclusion criteria (5 listed)
- Neurological deficits caused by non-stroke etiologies (e.g., brain tumor, traumatic brain injury, encephalitis).
- Presence of severe disturbance of consciousness, severe cognitive impairment, psychiatric disorders, or global aphasia at discharge/outpatient visit, preventing effective communication; neither the patient nor family can cooperate with follow-up or assessment.
- Combined with severe multi-organ failure (e.g., cardiac, hepatic, renal, respiratory), with an expected survival of less than 1 month, making completion of the 1-week follow-up impossible.
- Long-term bedridden without a fixed caregiver, with no confirmed contact for follow-up, or refusal to participate in telephone follow-up and mRS assessment.
- Incomplete clinical data, preventing baseline data collection.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Agreement Between Artificial Intelligence (AI)-Based and Manual Modified Rankin Scale (mRS) Assessments
Time frame: 7 days post-discharge or post-outpatient visit, ± 2 days
The weighted kappa coefficient quantifies the level of agreement between the Artificial Intelligence (AI)-generated Modified Rankin Scale (mRS) scores and the standardized manual mRS assessments performed by trained clinicians
3 secondary outcomes
Agreement Between AI-based and Manual Assessments of Dichotomized Modified Rankin Scale (mRS)
Time frame: 7 days post-discharge or post-outpatient visit, ± 2 days
Bland-Altman Limits of Agreement Between AI and Manual Modified Rankin Scale (mRS) Scores
Time frame: 7 days post-discharge or post-outpatient visit, ± 2 days
Diagnostic Performance of AI-Based vs. Manual Modified Rankin Scale (mRS) Dichotomization
Time frame: 7 days post-discharge or post-outpatient visit, ± 2 days
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Apr 2026.
- Individual participant dataSharing planned: No. Due to the protection of participant privacy and institutional review board requirements, individual participant data (IPD) will not be shared publicly.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Qingfeng Ma, MD study chair
Xuanwu Hospital, Beijing
Questions about this trial
What is this clinical trial studying?
An observational study of Stroke, run by Xuanwu Hospital, Beijing.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Qingfeng Ma, MD (+8613601069493, m.qingfeng@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement Between Artificial Intelligence (AI)-Based and Manual Modified Rankin Scale (mRS) Assessments (time frame: 7 days post-discharge or post-outpatient visit, ± 2 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Apr 2026.