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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| Measure | Description | Time Frame |
|---|---|---|
| Feasibility of standardized transcranial ultrasound acquisition in acute stroke | Feasibility will be measured as the proportion of patients in whom a diagnostic-quality transcranial ultrasound window is obtained (window quality grade 1 or 2). All examinations will follow the same standardized acquisition protocol, ensuring methodological consistency across operators. Diagnostic window success rate (%) will serve as the primary outcome. | At enrollment time (T0) |
| Measure | Description | Time Frame |
|---|---|---|
| Exam acquisition time under a standardized protocol | Time (in seconds) from probe placement to acquisition of the first diagnostic-quality sonographic frame, obtained using the standardized sonographic protocol. Results will be reported as median, interquartile range (IQR), and distribution. | At enrollment time (T0) |
| Measure | Description | Time Frame |
|---|---|---|
| Operator-level variability in acquisition performance using the standardized protocol | Comparison of diagnostic level images acquisition time across operators performing scans with the same standardized protocol. Results will evaluate reproducibility and ease of training. | At enrollment time (T0) |
| Operator-level variability in acquisition performance using the standardized protocol |
Inclusion Criteria:
Exclusion Criteria:
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The study population consists of adult patients (≥18 years) presenting with suspected acute stroke, either ischaemic or intracerebral haemorrhage. All participants undergo standard-of-care CT imaging to confirm diagnosis. Only supratentorial strokes are included, as patients with infratentorial haemorrhage or isolated subarachnoid haemorrhage are excluded.
Eligible patients must be able to undergo transtemporal transcranial Doppler ultrasound using a standardized acquisition protocol. The cohort includes both EMS-transported and in-hospital stroke cases. Participants represent a typical real-world acute stroke population, including individuals with varying stroke severity, vascular risk factors, and clinical profiles.
No modifications to clinical management occur as part of the study. Ultrasound acquisition and data collection are purely observational and performed after CT confirmation.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| RENATO SIMONETTI, MD | Contact | +34934893000 | 6660 | renato.simonetti@vallhebron.cat |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Universitario Vall D'Hebron | Recruiting | Barcelona | Catalonia | 08035 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41263848 | Result | 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. |
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IPD will not be shared because the study collects sensitive clinical and imaging information, and no formal data-sharing agreements or secure access frameworks are currently in place. Sharing policies may be reconsidered if a structured multicenter collaboration with appropriate data protections is established.
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| Accuracy of AI-based classification of intracerebral haemorrhage using standardized TCD acquisitions |
Evaluation of artificial intelligence models (CNN and transformer-based architectures) trained on ultrasound images acquired with a uniform standardized protocol. Performance will be assessed against CT-confirmed diagnosis. |
| From enrollment to the completion of imaging data collection at 16 months |
Comparison of transtemporal window acquisition success rate across operators performing scans with the same standardized protocol. Results will evaluate reproducibility and ease of training. |
| At enrollment time (T0) |
| ID | Term |
|---|---|
| D020521 | Stroke |
| D002543 | Cerebral Hemorrhage |
| D000083242 | Ischemic Stroke |
| D000083302 | Hemorrhagic Stroke |
| ID | Term |
|---|---|
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D020300 | Intracranial Hemorrhages |
| D006470 | Hemorrhage |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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