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

NCT ID: NCT07319013

Last Updated: 2026-01-06

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-03-14
• Study Completion Date: 2027-06-30

Brief Summary

The goal of this observational study is to evaluate whether transcranial Doppler ultrasound, combined with artificial intelligence (AI), can help identify intracerebral haemorrhage (ICH) in people with acute stroke (both men and women, adults of all ages) within 48 hours of symptom onset.

The main questions it aims to answer are:

Is it feasible to perform standardized protocol transcranial ultrasound in acute stroke patients? Can AI models trained on ultrasound images accurately distinguish haemorrhagic stroke ("ICH suspected") from non-haemorrhagic stroke? There is no comparison group, because all participants will undergo both CT (as standard care) and ultrasound (research imaging), and the AI models will compare their ultrasound-based predictions against CT-confirmed diagnoses.

Participants will:

undergo a non-invasive transcranial ultrasound scan after CT confirms the type of stroke allow researchers to collect coded ultrasound images for AI model training provide clinical and imaging information (already collected as part of routine care) to help evaluate factors related to diagnostic accuracy No treatments or changes to clinical care will be introduced as part of the study.

Detailed Description

Stroke is a medical emergency that can be caused either by a blocked blood vessel (ischaemic stroke) or by bleeding inside the brain (haemorrhagic stroke). These two types of stroke require very different treatments, and identifying which one is occurring as quickly as possible is essential.

Currently, the only reliable way to distinguish between these two types of stroke is with a brain scan such as a CT scan. However, CT is not always available immediately, especially in prehospital settings or in hospitals without 24/7 imaging access. As a result, patients may experience delays before receiving the correct treatment.

This study aims to explore whether a simple ultrasound scan of the brain, performed through the skull, can help identify haemorrhagic stroke more quickly. This technique is called transcranial Doppler ultrasound (TCD). It is fast, non-invasive, and uses no radiation.

A total of 500 patients with suspected stroke within 48 hours of symptom onset will be included. After the standard CT scan confirms the diagnosis, each participant will undergo a brief ultrasound scan following a structured protocol.

The ultrasound images will then be used to train and test artificial intelligence (AI) models, which will learn to recognize patterns associated with haemorrhagic stroke. These AI models will compare the ultrasound images with CT results and try to predict whether a bleed is present ("ICH suspected") or not.

The main goals of the study are:

To determine whether portable ultrasound can be performed reliably and consistently in real stroke patients.

To evaluate whether AI can support clinicians by interpreting these ultrasound images and distinguishing between haemorrhagic and non-haemorrhagic strokes.

All other clinical information-such as symptoms, timing of arrival, and medical history-will also be collected to understand which factors may influence the performance of ultrasound and AI.

Importantly, the ultrasound does not replace standard medical care and will not influence the treatment that patients receive. It is performed only for research purposes. The CT scan remains the reference test for diagnosis.

By combining ultrasound with AI, this project hopes to pave the way for future systems capable of assisting paramedics or physicians in identifying haemorrhagic stroke earlier, especially in settings where CT is not immediately available. Earlier recognition may help reduce delays in blood pressure management or treatment reversal for patients taking anticoagulants.

Conditions

Stroke; Intracerebral Hemorrhage; Intracerebral Hemorrhage Basal Ganglia; Intracerebral Haemorrhage

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Acute stroke patients

Single cohort of adults with suspected acute stroke (\<48 hours from symptom onset), including both ischaemic and intracerebral haemorrhagic stroke. All participants will undergo standard diagnostic evaluation with computed tomography (CT). A research transcranial Doppler ultrasound (TCD) examination will then be performed using a standardized acquisition protocol. Coded sonographic data will be used to train and evaluate artificial intelligence (AI) models for the classification of intracerebral haemorrhage ("ICH suspected" vs. "No ICH"). No intervention or change in clinical management is introduced as part of the study.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• Adult patients (age ≥18 years).
• Clinical diagnosis of acute stroke (ischemic or intracerebral hemorrhage).
• Able to undergo transtemporal transcranial ultrasound according to the standardized protocol (no clinical instability)
• Informed consent obtained from the patient or legally authorized representative, per local regulations.

Exclusion Criteria

• Infratentorial hemorrhage (e.g., cerebellar or brainstem hemorrhage), due to limitations of transtemporal insonation.
• Isolated subarachnoid hemorrhage without parenchymal involvement.
• Hemodynamic instability or medical conditions requiring immediate life-saving intervention that preclude safe ultrasound recording.
• Known skull defects or prior craniectomy on the side required for contralateral insonation.
• Any condition that, in the opinion of the investigators, would interfere with protocol adherence or data accuracy.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Hospital Universitari Vall d'Hebron Research Institute

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Hospital Universitario Vall D'Hebron

Barcelona, Catalonia, Spain

Site Status: RECRUITING

Countries

Spain

Central Contacts

RENATO SIMONETTI, MD

Role: CONTACT

renato.simonetti@vallhebron.cat

+34934893000 ext. 6660

Facility Contacts

RENATO SIMONETTI, MD

Role: primary

renato.simonetti@vallhebron.cat

+34934893000 ext. 6660

References

Simonetti R, Canals P, Gonzalez Riveros JD, Alanis-Bernal M, Pancorbo O, Rodriguez-Luna D. Feasibility of an AI-assisted transcranial duplex sonography protocol for early detection of intracerebral haemorrhage: the HYPER-AI-SCAN single-centre prospective study. BMJ Open. 2025 Nov 19;15(11):e102903. doi: 10.1136/bmjopen-2025-102903.

Reference Type: RESULT

PMID: 41263848 (View on PubMed)

Other Identifiers

PR(AG)015/2025

Identifier Type: -

Identifier Source: org_study_id