Optimizing Thrombectomy in Acute Ischemic Stroke Using Artificial Intelligence

AI-STROKE – Optimizing Acute Ischemic Stroke Diagnostics Using Artificial Intelligence.The main aim of the this prospective observational multi-center study is 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. The project is led from Oslo UH in collaboration with Østfold, Vestre Viken and Innlandet HT.Secondary aims: 1. 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 mCTA and MRI (DWI and MR Angiography) assessed by neuroradiologists. 2. 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. 3. 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. 4. 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. Number of subjects: 300 Study centers: Oslo University Hospital, Vestre Viken Hospital and Østfold Hospital Trust. Duration of Study participation: - Enrollment: 24 months - Follow-up: 3 months - Total study duration 27 months 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 baseline registration period started in December 2021. Due to delays from Sykehuspartner the start-up of the the implementation of AI-based tools in the assessment of medium and large vessel occlusion and selection of candidates for thrombectomy in Østfold Hospital Trust started autumn 2023 and in different hospitals in Vestre Viken Hospital Trust by the turn of the year and Lillehammer is planning to start in 2024. The project is coordinated with a parallell project in the Northern Norway Regional Health Authority, involving all hospitals in the northern region.

AI-STROKE - Optimizing Acute Ischemic Stroke Diagnostics Using Artificial Intelligence.The main aim of the this prospective observational multi-center study is 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. The project is led from Oslo UH in collaboration with Østfold and Vestre Viken Hospital Trust.Secondary aims: 1. 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 mCTA and MRI (DWI and MR Angiography) assessed by neuroradiologists. 2. 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. 3. 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. 4. 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. Number of subjects: 300 Study centers: Oslo University Hospital, Vestre Viken Hospital and Østfold Hospital Trust. Duration of Study participation: - Enrollment: 24 months - Follow-up: 3 months - Total study duration 27 months 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 baseline registration period started in December 2021. Due to delays from Sykehuspartner we are still waiting for the start-up of the the implementation of AI-based tools in the assessment of medium and large vessel occlusion and selection of candidates for thrombectomy in Vestre Viken Hospital Trust. Østfold Hospital Trust has been recruited to strengthen the inclusion. The project is coordinated with a parallell project in the Northern Norway Regional Health Authority, involving all hospitals in the northern region.

AI-STROKE - Optimizing Acute Ischemic Stroke Diagnostics Using Artificial Intelligence.The main aim of the this prospective observational multi-center study 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: 1. 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 mCTA and MRI (DWI and MR Angiography) assessed by neuroradiologists. 2. 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. 3. 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. 4. 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. Number of subjects: 300 Study centers: Oslo University Hospital, Vestre Viken Hospital Trust Duration of Study participation: - Enrollment: 24 months - Follow-up: 3 months - Total study duration 27 months 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 6-months baseline registration period started in December 2021. After 6 months the implementation of AI-based tools in the assessment of medium and large vessel occlusion and selection of candidates for thrombectomy will be implemented.