An Artificial Intelligence-Assisted Magnetic Resonance Imaging Diagnostic Strategy in a Tertiary Stroke Center-a Diagnostic Accuracy Study
An Artificial Intelligence-Assisted Magnetic Resonance Imaging Diagnostic Strategy in a Tertiary Stroke Center-a Diagnostic Accuracy Study
Quality improvement study with prospective observational design. The study monitors the diagnostic accuracy of an AI-assisted resident radiologist-termed the AI-ResRad diagnostic strategy-compared to an on-call specialist neuroradiologist-termed the SpecNeuroRad strategy-in interpreting stroke MRIs in patients with known onset.
The study includes a pre-planned sub-study evaluating the diagnostic accuracy of neurologists and AI-assisted neurologists.
Current clinical practice and the supporting evidence base rely on interpretations by specialist neuroradiologists. Modern radiology departments face increasing imaging demands while contending with limited resources-including a shortage of specialist neuroradiologists. In the ideal setting, patients are evaluated in real time by a vascular neurologist and a neuroradiologist, who synchronously integrate clinical and imaging findings. In such cases, thrombolysis decisions can be re-evaluated concurrently with MRI acquisition, initiating treatment within minutes of scan completion. Although modern stroke MRI protocols can be completed in as little as 10 minutes, these rapid-response team activations often consume a disproportionate share of specialist time and availability. Consequently, real-world clinical practice frequently involves alternative team configurations, including resident radiologists, resident neurologists, and remote specialist consultations-compositions that vary depending on the on-call team's experience, time of day, and day of the week.
Artificial intelligence (AI) can support the team with image interpretation, potentially optimizing time and resources. Recent studies have explored the role of AI-assisted stroke workflows and its ability to accurately detect ischemic lesions and hemorrhagic stroke-demonstrating promising encouraging diagnostic performance. However, there remains a need for prospective studies evaluating the real-world diagnostic accuracy of AI assistance as applied within its intended clinical use context To further understand the potential contributions of AI-assistance and resident radiologist interpretations, we designed the AID-STROKE accuracy study, under the Danish Quality Improvement legal and design framework.
Sub-study: An Artificial Intelligence-Assisted Neurologist-based Diagnostic Strategy in Magnetic Resonance Imaging of Acute Stroke Patients with Known Onset-a Diagnostic Accuracy Study
This pre-specified sub-study will be conducted in patients received at one of the hospitals (Gødstrup Regional Hospital)
Inclusion Criteria:
Exclusion Criteria:
Participation in the neurologists' sub-study is limited to participants enrolled at one of the two participating sites at Gødstrup Regional Hospital.