Actively Recruiting

Age: 18Years +
All Genders
ID07319013

HYPER-AI-SCAN: AI-Assisted Transcranial Ultrasound for Early Detection of Intracerebral Hemorrhage in Acute Stroke

Led by Hospital Universitari Vall d'Hebron Research Institute · Updated on 2026-01-06

500

Participants Needed

1

Research Sites

26 weeks

Total Duration

On this page

AI-Summary

What this Trial Is About

Researchers are evaluating whether combining transcranial Doppler ultrasound with artificial intelligence (AI) can help detect intracerebral hemorrhage (ICH) in adults with acute stroke within 48 hours of symptom onset. The study focuses on the feasibility of performing standardized ultrasound scans in stroke patients and whether AI can accurately distinguish hemorrhagic stroke from non-hemorrhagic stroke by comparing ultrasound predictions with CT scan results, which are the standard diagnostic method. Participants will undergo a standard CT scan to confirm stroke type, followed by a non-invasive transcranial Doppler ultrasound scan using a structured protocol. The ultrasound images will be coded and used to train and test AI models designed to recognize patterns linked to hemorrhagic stroke. No treatments or changes to clinical care are introduced as part of this observational research. During the study, participants will provide clinical and imaging information collected as part of routine care, while researchers will evaluate the feasibility of ultrasound acquisition and the accuracy of AI-based classification. The main outcome is the ability to perform standardized ultrasound scans, with secondary measures including exam acquisition time and AI accuracy. The ultrasound is performed only for research and does not affect participants' treatment. The study aims to improve early stroke diagnosis, especially where CT is not immediately available.

CONDITIONS

Brief Title

AI-assisted Transcranial Duplex Sonography for Early Detection of Intracerebral Haemorrhage: HYPER-AI-SCAN

Who Can Participate

Age: 18Years +
All Genders

Eligibility Criteria

Eligible

You may qualify if you...

  • Adult patients (age �3e�3d 18 years)
  • Clinical diagnosis of acute stroke (ischemic or intracerebral hemorrhage)
  • Able to undergo transtemporal transcranial ultrasound following standardized protocol
  • Informed consent obtained from patient or legally authorized representative
Not Eligible

You will not qualify if you...

  • Infratentorial hemorrhage such as cerebellar or brainstem hemorrhage
  • Isolated subarachnoid hemorrhage without brain tissue involvement
  • Hemodynamic instability or urgent medical conditions preventing safe ultrasound
  • Known skull defects or prior craniectomy on the ultrasound side
  • Any condition that may interfere with following the study protocol or affect data accuracy

AI-Screening

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Your Study Journey

Screening

Duration - 2 to 4 weeks

Participants are screened for eligibility to participate in the trial.

1 visit (in-person)

Diagnostic Evaluation

Duration - Within 48 hours of symptom onset

Participants undergo a standard CT scan to confirm the type of stroke, followed by a research transcranial Doppler ultrasound (TCD) performed using a standardized protocol for study purposes.

1 visit (in-person) at enrollment

Long-term Monitoring

Duration - Up to 16 months

Data from ultrasound images and CT results are used to train and evaluate AI models over the study period without affecting clinical care.

No additional visits required for this study phase

Trial Site Locations

Total: 1 location

1

Hospital Universitario Vall D'Hebron

Barcelona, Catalonia, Spain, 08035

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Research Team

R

RENATO SIMONETTI, MD

How is the study designed?

Study Type

OBSERVATIONAL

Masking

N/A

Allocation

N/A

Model

N/A

Primary Purpose

N/A

Number of Arms

1

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Published Research Related To This Trial

Feasibility of an AI-assisted transcranial duplex sonography protocol for early detection of intracerebral haemorrhage: the HYPER-AI-SCAN single-centre prospective study.

Renato Simonetti, Pere Canals, Jesus David Gonzalez Riveros...

https://pubmed.ncbi.nlm.nih.gov/41263848