Stroke, AcuteAI-STROKENCT05652933
Optimizing Acute Ischemic Stroke Diagnostics Using Artificial Intelligence
An observational study of Stroke, Acute, run by Oslo University Hospital at 3 sites in Norway.
Trial summary
Prospective observational multi-center study with the aim to organise and simplify the care pathway through a pragmatic approach to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Stroke, Acute
- What is being tested?
- Software for identifying vessel occlusion, infarct volume and penumbra
- How many people
- 300 participants
- Where
- 3 sitesNorway
- Study dates
- 10 Dec 2021to 31 Dec 2025
- How long will I take part?
- 3 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients admitted with acute ischemic stroke at participating hospitals
Who may be able to join
- Patients with ischemic stroke.
- All stroke severities and vascular distributions are eligible.
- Informed written consent signed by the patient, verbal consent from the patient as witnessed by a non-participating health care person or consent by the signature of the patient's family must be provided before inclusion. Patients for whom no informed consent can be obtained will not be included in the study but will be treated according to standard guidelines.
Who cannot join
- Patients not available for follow-up assessments (e.g. non-resident).
Trial locations and contacts
3 sites in 1 country.
Norway3 sites, 3 recruiting
- Recruiting
Vestre Viken Hospital Trust
Drammen
Bjørn Anton Graff, +47 32 80 30 00, bjgraf@vestreviken.no
- Recruiting
Oslo University Hospital
Oslo
Anne Hege Aamodt, MD PhD Prof, +47 23 07 00 00, anhaam@ous-hf.no
Brian Enriquez, MD, +47 23 07 00 00, brienr@ous-hf.no
- Recruiting
Østfold Hospital Trust
Sarpsborg
Linn Heitmann, +47 69 86 00 00, Linn.Aarbakke.Heitmann@so-hf.no
Jostein Gleditsch, +47 69 86 00 00, Jostein.Gleditsch@so-hf.no
About this study
Prospective observational multi-center study with the aim to organise and simplify the care pathway through a pragmatic approach to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence.
Read the detailed description
Thrombectomy in acute ischemic stroke is highly effective and cost-effective. As of today, too few patients have access to thrombectomy. There is an urgent need to improve the diagnostics so that all eligible stroke patients have their occlusion detected fast enough and are offered thrombectomy when indicated. Machine learning based imaging techniques have recently been shown to provide improved diagnostic with automated methods for detection of vessel occlusion and ischemic lesions by use of artificial intelligence. We will perform a prospective interventional study in acute ischemic stroke patients with the aim to organize and simplify the care pathway through a pragmatic approach to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence. By using multiphase CT angiography and software at two primary stroke centres the utility of automatically evaluation of images will be compared to standard care. All images will in parallell be assessed by neuroradiologists at the comprehensive stroke centre.
The main objective is to organize and simplify the care pathway to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence.
The secondary objectives are to assess: 1) the diagnostic accuracy of mCTA in detection of vessel occlusion in ischemic stroke using AI-based analysis tools compared gold standard of MRI, 2) the percentage of eligible patients who receive EVT using AI-based analysis compared to standard care diagnostics 3) time from onset to recanalization, and 4) functional outcome in acute ischemic stroke patients treated with EVT who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
Hypotheses: Novel AI-based image analysis tools applied to already available standard CT based imaging techniques can a) improve acute stroke diagnostics and b) increase the number of patients treated by EVT.
The main aim of the project is to organise and simplify the care pathway through a pragmatic approach to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence.
Secondary aims:
- To assess if the use of AI-based image analysis tools in radiological diagnostics in primary stroke centres can reduce the time from onset to recanalization in acute ischemic stroke patients treated with EVT.
- To assess the diagnostic accuracy of mCTA in detection of medium and large vessel occlusion ischemic stroke using AI-based analysis tools compared to assessment by the gold standard of MRI (DWI and MR Angiography) assessed by neuroradiologists.
- To assess the diagnostic accuracy of mCTA in detection of medium and large vessel occlusion ischemic stroke using AI-based analysis tools compared to assessment by standard care.
- To assess if the use of available AI-based analysis tools applied to mCTA can increase the number of stroke patients eligible for and offered EVT.
- To compare functional outcome and patient related outcome measures 3 months after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools to stroke patients diagnosed by standard care.
Endpoints:
Primary Endpoints:
- Time from the start of CT scan of patients at the local hospital to radiological diagnosis in acute stroke patients with LVO and MeVO in periods with the use of AI software compared to periods with standard care.
Secondary endpoints:
- Time from the start of CT scan of patients at the local hospital to start of thrombectomy in patients identified with LVO and MeVO in periods with the use of AI software compared to periods with standard care.
- Time from symptom onset to start of thrombectomy in patients identified with LVO and MeVO in periods with the use of AI software compared with proportion of patients identified with LVO and MeVO diagnosed by standard care.
- Proportion of patients identified with LVO and MeVO in periods with the use of AI software compared with proportion of patients identified with LVO and MeVO diagnosed by standard care.
- Proportion of patients identified with LVO and MeVO in periods with the use of AI software compared to assessment by neuroradiologists.
- Proportion of patients treated with thrombectomy in MeVO in periods with the use of AI software compared with proportion of patients identified with LVO and MeVO diagnosed by standard care.
- Functional outcome 3 months after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
- Patient related outcome measures 3 months after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
The present study is part of a prospective observational study of the thrombectomy service with collaboration between stroke units and radiological departments at primary and comprehensive stroke centres - the Oslo Acute Revascularization Stroke Study (OSCAR) (REK 2015/1844, EudraCT number 2018-004691-36). Data has already been collected since January 2017 in patients treated with EVT at Oslo University Hospital and by nearly 1100 patients treated with EVT have been included. The database contains detailed information on logistics, transport, clinical, radiological data, and treatment including rehabilitation from baseline to 3-month follow-up is registered prospectively.
The study will start with a 12-month period with registration before the implementation of the AI software. Data from this period and from the OSCAR study will be compared to the data collected after the implementation of the AI software. We will start the study at Drammen Hospital and will consecutively implement it at the other hospitals in Vestre Viken Hospital Trust and Østfold Hospital Trust. Data will be registered for at least 18 months after the implementation of the AI software. The length of the inclusion phase will be adjusted according to the inclusion rate.
- Conditions
- Stroke, Acute
- Being tested
- Software for identifying vessel occlusion, infarct volume and penumbra
- Medical subject headings
- Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Software for identifying vessel occlusion, infarct volume and penumbraDiagnostic test
Assessment of the AI tool software
What is being measured
The primary outcome is the main question the study is designed to answer.
Time from the start of CT scan of patients at the local hospital to radiological diagnosis in acute stroke patients with large and medium vessel occlusion in periods with the use of AI software compared to periods with standard care.
Minutes
Measured: Day 0
Secondary outcomes (6)
Time from the start of CT scan of patients at the local hospital to start of thrombectomy in patients identified with large and medium vessel occlusion in periods with the use of AI software compared to periods with standard care.
Minutes
Measured: Day 0
Time from symptom onset to start of thrombectomy in patients identified with LVO large and medium vessel occlusion in periods with the use of AI software compared with proportion of patients identified with LVO and MeVO diagnosed by standard care.
Minutes
Measured: Day 0
Proportion of patients identified with large and medium vessel occlusion in periods with the use of AI software compared with proportion of patients identified with large and medium vessel occlusion diagnosed by standard care.
Number of patients
Measured: Day 0
Proportion of patients treated with thrombectomy in large and medium vessel occlusion in periods with the use of AI software compared with proportion of patients identified with large and medium vessel occlusion diagnosed by standard care.
Number of patients
Measured: Day 0
Functional outcome at 90 days after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
Number of participants with independent functioning on the modified Rankin Scale (mRS 0 to 6), as defined by a score of 0-2. The modified Rankin Scale (mRS) is a valid and reliable clinician-reported measure of global disability that has been widely applied for evaluating recovery from stroke. It is a scale used to measure functional recovery (the degree of disability or dependence in daily activities) of people who have suffered a stroke. mRS scores range from 0 (best outcome) to 6 (worst outcome), with 0 indicating no residual symptoms; 5 indicating bedbound, requiring constant care; and 6 indicating death.
Measured: 90 days
Health-related quality of life at 90 days after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
Health-related quality of life, as measured by the EQ-5D-5L at Day 90. The EQ-5D-5L (EuroQol 5-Dimensional 5-Level) is a generic instrument for describing and valuing health. It is based on a descriptive system that defines health in terms of five dimensions: Mobility, Self-Care, Usual Activities, Pain/Discomfort, and Anxiety/Depression. Each dimension has five response categories corresponding to: no problems, slight, moderate, severe and extreme problems. The respondents will also rate their overall health on the day of the interview on a 0-100 visual analogue scale (EQ-VAS, higher scores mean better outcomes).
Measured: 90 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Dec 2022
- Study start10 Dec 2021
- Primary completion15 Mar 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Stroke, AcuteStudies of the same condition, open and under way.
- More trials run by Oslo University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 300 participants (estimated)
- Target duration
- 3 Months
Sponsor and investigators
- Lead sponsor
- Oslo University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Vestre Viken Hospital Trust, University Hospital of North Norway, University of Calgary
- Responsible party
- Principal investigator, Anne Hege Aamodt, Oslo University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05652933
- Sponsor's protocol ID
- 282961
- Acronym
- AI-STROKE
- Status last verified
- Oct 2023
- First submitted
- 22 Feb 2022
- Last updated
- 17 Oct 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Optimizing Acute Ischemic Stroke Diagnostics Using Artificial Intelligence
- ClinicalTrials.gov ID
- NCT05652933
- Acronym / sponsor ID
- AI-STROKE / 282961
- Conditions
- Stroke, Acute
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Oslo University HospitalUniversity, hospital or other
- Collaborators
- Vestre Viken Hospital Trust, University Hospital of North Norway, University of Calgary
- Study start
- 10 Dec 2021 (actual)
- Primary completion
- 15 Mar 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 15 Dec 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Prospective observational multi-center study with the aim to organise and simplify the care pathway through a pragmatic approach to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence.
Detailed description
Thrombectomy in acute ischemic stroke is highly effective and cost-effective. As of today, too few patients have access to thrombectomy. There is an urgent need to improve the diagnostics so that all eligible stroke patients have their occlusion detected fast enough and are offered thrombectomy when indicated. Machine learning based imaging techniques have recently been shown to provide improved diagnostic with automated methods for detection of vessel occlusion and ischemic lesions by use of artificial intelligence. We will perform a prospective interventional study in acute ischemic stroke patients with the aim to organize and simplify the care pathway through a pragmatic approach to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence. By using multiphase CT angiography and software at two primary stroke centres the utility of automatically evaluation of images will be compared to standard care. All images will in parallell be assessed by neuroradiologists at the comprehensive stroke centre.
The main objective is to organize and simplify the care pathway to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence.
The secondary objectives are to assess: 1) the diagnostic accuracy of mCTA in detection of vessel occlusion in ischemic stroke using AI-based analysis tools compared gold standard of MRI, 2) the percentage of eligible patients who receive EVT using AI-based analysis compared to standard care diagnostics 3) time from onset to recanalization, and 4) functional outcome in acute ischemic stroke patients treated with EVT who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
Hypotheses: Novel AI-based image analysis tools applied to already available standard CT based imaging techniques can a) improve acute stroke diagnostics and b) increase the number of patients treated by EVT.
The main aim of the project is to organise and simplify the care pathway through a pragmatic approach to acute stroke imaging powered by cutting edge advances in image processing and artificial intelligence.
Secondary aims:
- To assess if the use of AI-based image analysis tools in radiological diagnostics in primary stroke centres can reduce the time from onset to recanalization in acute ischemic stroke patients treated with EVT.
- To assess the diagnostic accuracy of mCTA in detection of medium and large vessel occlusion ischemic stroke using AI-based analysis tools compared to assessment by the gold standard of MRI (DWI and MR Angiography) assessed by neuroradiologists.
- To assess the diagnostic accuracy of mCTA in detection of medium and large vessel occlusion ischemic stroke using AI-based analysis tools compared to assessment by standard care.
- To assess if the use of available AI-based analysis tools applied to mCTA can increase the number of stroke patients eligible for and offered EVT.
- To compare functional outcome and patient related outcome measures 3 months after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools to stroke patients diagnosed by standard care.
Endpoints:
Primary Endpoints:
- Time from the start of CT scan of patients at the local hospital to radiological diagnosis in acute stroke patients with LVO and MeVO in periods with the use of AI software compared to periods with standard care.
Secondary endpoints:
- Time from the start of CT scan of patients at the local hospital to start of thrombectomy in patients identified with LVO and MeVO in periods with the use of AI software compared to periods with standard care.
- Time from symptom onset to start of thrombectomy in patients identified with LVO and MeVO in periods with the use of AI software compared with proportion of patients identified with LVO and MeVO diagnosed by standard care.
- Proportion of patients identified with LVO and MeVO in periods with the use of AI software compared with proportion of patients identified with LVO and MeVO diagnosed by standard care.
- Proportion of patients identified with LVO and MeVO in periods with the use of AI software compared to assessment by neuroradiologists.
- Proportion of patients treated with thrombectomy in MeVO in periods with the use of AI software compared with proportion of patients identified with LVO and MeVO diagnosed by standard care.
- Functional outcome 3 months after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
- Patient related outcome measures 3 months after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
The present study is part of a prospective observational study of the thrombectomy service with collaboration between stroke units and radiological departments at primary and comprehensive stroke centres - the Oslo Acute Revascularization Stroke Study (OSCAR) (REK 2015/1844, EudraCT number 2018-004691-36). Data has already been collected since January 2017 in patients treated with EVT at Oslo University Hospital and by nearly 1100 patients treated with EVT have been included. The database contains detailed information on logistics, transport, clinical, radiological data, and treatment including rehabilitation from baseline to 3-month follow-up is registered prospectively.
The study will start with a 12-month period with registration before the implementation of the AI software. Data from this period and from the OSCAR study will be compared to the data collected after the implementation of the AI software. We will start the study at Drammen Hospital and will consecutively implement it at the other hospitals in Vestre Viken Hospital Trust and Østfold Hospital Trust. Data will be registered for at least 18 months after the implementation of the AI software. The length of the inclusion phase will be adjusted according to the inclusion rate.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 3 Months
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients admitted with acute ischemic stroke at participating hospitals
Key inclusion criteria (3 listed)
- Patients with ischemic stroke.
- All stroke severities and vascular distributions are eligible.
- Informed written consent signed by the patient, verbal consent from the patient as witnessed by a non-participating health care person or consent by the signature of the patient's family must be provided before inclusion. Patients for whom no informed consent can be obtained will not be included in the study but will be treated according to standard guidelines.
Key exclusion criteria (1 listed)
- Patients not available for follow-up assessments (e.g. non-resident).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Software for identifying vessel occlusion, infarct volume and penumbraAssessment of the AI tool software | Diagnostic test | – |
Primary outcome
Time from the start of CT scan of patients at the local hospital to radiological diagnosis in acute stroke patients with large and medium vessel occlusion in periods with the use of AI software compared to periods with standard care.
Time frame: Day 0
Minutes
6 secondary outcomes
Time from the start of CT scan of patients at the local hospital to start of thrombectomy in patients identified with large and medium vessel occlusion in periods with the use of AI software compared to periods with standard care.
Time frame: Day 0
Time from symptom onset to start of thrombectomy in patients identified with LVO large and medium vessel occlusion in periods with the use of AI software compared with proportion of patients identified with LVO and MeVO diagnosed by standard care.
Time frame: Day 0
Proportion of patients identified with large and medium vessel occlusion in periods with the use of AI software compared with proportion of patients identified with large and medium vessel occlusion diagnosed by standard care.
Time frame: Day 0
Proportion of patients treated with thrombectomy in large and medium vessel occlusion in periods with the use of AI software compared with proportion of patients identified with large and medium vessel occlusion diagnosed by standard care.
Time frame: Day 0
Functional outcome at 90 days after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
Time frame: 90 days
Health-related quality of life at 90 days after EVT in stroke patients who had their initial radiological diagnosis using AI-based image analysis tools compared to stroke patients diagnosed by standard care.
Time frame: 90 days
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Oct 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Stroke, Acute, run by Oslo University Hospital at 3 sites in Norway.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 17 Oct 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Norway. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Time from the start of CT scan of patients at the local hospital to radiological diagnosis in acute stroke patients with large and medium vessel occlusion in periods with the use of AI software compared to periods with standard care. (time frame: Day 0). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Oct 2023.