Optimizing Acute Ischemic Stroke Diagnostics Using Artificial Intelligence
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 300
- 试验地点
- 3
- 主要终点
- 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.
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •Patients not available for follow-up assessments (e.g. non-resident).
结局指标
主要结局
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.
时间窗: Day 0
Minutes
次要结局
- 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.(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.(90 days)
- 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.(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.(Day 0)
- 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.(90 days)
- 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.(Day 0)
研究者
Anne Hege Aamodt
Coordinating Investigator
Oslo University Hospital