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NCT Number: NCT07538492

Clinical Validation of an Artificial Intelligence-Based G-FAST Score in Patients With Stroke

This study aims to validate the clinical performance of an artificial intelligence (AI)-based automatic assessment system for the G-FAST score. The core comparison is the consistency and accuracy between AI-generated G-FAST results and standardized manual G-FAST assessments performed by trained professionals. The goal is to provide a convenient, efficient, and objective tool for acute stroke screening and early identification, reduce the subjective variability of manual scoring, and optimize the pre-hospital and in-hospital stroke assessment workflow.

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Key information

Conditions

Stroke Brain Diseases Cardiovascular Diseases Central Nervous System Diseases Cerebrovascular Disorders Nervous System Diseases Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Aged ≥ 18 years, of either sex.
  • Clinically diagnosed with stroke, and confirmed by cranial CT/MRI to have ischemic or hemorrhagic stroke.
  • Onset within 7 days.
  • Alert and oriented, able to cooperate with standardized video and audio data collection.
  • The patient or their legally authorized representative understands the study and voluntarily provides written informed consent (including consent for audio-visual data collection).

Exclusion criteria

  • Neurological deficits caused by non-stroke etiologies (e.g., brain tumor, traumatic brain injury, encephalitis).
  • Patients with impaired consciousness, severe cognitive dysfunction, or psychiatric disorders that prevent cooperation with video collection and scale assessment.
  • Patients with severe visual or hearing impairment, or global aphasia, who are unable to follow instructions.
  • Critically ill patients requiring immediate cardiopulmonary resuscitation or endotracheal intubation, making video and audio data collection impossible.
  • Patients with severe facial or limb deformities, or large-area dressings that severely interfere with camera data collection.
  • Patients with unilateral or bilateral upper limb amputation, severe deformity, unhealed fracture, joint fixation, or severe contracture.

Treatment and study plan

Primary outcomes

  1. Agreement between AI-generated and physician-scored G-FAST scale assessments

    Time frame: within 7 days of acute stroke onset

    The agreement between the scores generated by the artificial intelligence (AI) system and the scores assigned by neurologists on G-FAST scale will be evaluated using weighted Kappa coefficients.

Secondary outcomes

  1. Agreement of AI System vs. Neurologists in Binary G-FAST Classification (Score ≥3 vs. <3)

    Time frame: within 7 days of acute stroke onset

    Kappa coefficient will be calculated to evaluate the agreement between the artificial intelligence (AI) system and neurologist experts in the binary classification of G-FAST scale scores, defined as high risk (total score ≥3) vs. low risk (total score <3) for large vessel occlusion stroke.

  2. Bland-Altman Agreement Limit Analysis

    Time frame: within 7 days of acute stroke onset

    A Bland-Altman plot will be constructed, with the difference between manual scores and AI scores on the vertical axis and the mean of the two scores on the horizontal axis. The limits of agreement (mean difference ± 1.96 × standard deviation) will be calculated.

  3. Diagnostic performance analysis

    Time frame: within 7 days of acute stroke onset

    Taking the manual score as the gold standard, a 2×2 contingency table was constructed to calculate the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and Youden index of the AI scoring system for stratifying the G-FAST score (LVO ≥3 vs. non-LVO <3). The ROC curve was plotted and the AUC was calculated.

Study contacts

Contact information is provided by the study sponsor or research team.

Qingfeng Ma, MD

CONTACT

[email protected]

+8613601069493

Zixin Wang, MD Candidate

CONTACT

[email protected]

+8615031041048

Sponsors and collaborators

Lead sponsor

Xuanwu Hospital, Beijing

Other

Collaborators

  • Beijing Tiantan Hospital
  • Capital Medical University
  • People's Hospital of Beijing Daxing District
  • The First Hospital of Fangshan District,Beijing

Registry information

Important dates

Study start
2026
Primary completion
2028
Study completion
2028
First posted
Apr 20, 2026
Registry last updated
Apr 20, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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