Intracerebral Haemorrhage, Intracerebral Hemorrhage, Intracerebral Hemorrhage Basal Ganglia, Stroke
Conditions
Keywords
stroke, intracerebral hemorrage, Ischemic Stroke, Hemorrhagic Stroke, transcranial ultrasound, Transcranial Doppler, Portable Ultrasound, Neurosonology, Artificial Intelligence, Machine Learning, Diagnostic Accuracy, Prehospital Care, Feasibility Study, Deep Learning
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.
Interventions
None listed
Sponsors
Hospital Universitari Vall d'Hebron Research Institute
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of standardized transcranial ultrasound acquisition in acute stroke | At enrollment time (T0) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Exam acquisition time under a standardized protocol | At enrollment time (T0) | 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. |
| Accuracy of AI-based classification of intracerebral haemorrhage using standardized TCD acquisitions | From enrollment to the completion of imaging data collection at 16 months | 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. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Operator-level variability in acquisition performance using the standardized protocol | At enrollment time (T0) | Comparison of diagnostic level images acquisition time across operators performing scans with the same standardized protocol. Results will evaluate reproducibility and ease of training. |
Countries
Spain
Contacts
Primary ContactRENATO SIMONETTI, MD
Outcome results
None listed