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RapidAI Compared With Human Readers of Acute Stroke Imaging for Detection of Intracranial Vessel Occlusion
Lee-Anne Slater1,2, Nandhini Ravintharan1, Stacy Goergen1,2
1Monash Health Imaging Monash Health Clayton Victoria Australia.
Insights
Neurologists and radiologists demonstrated higher accuracy than RapidAI software in detecting large-vessel occlusions (LVOs) and medium-vessel occlusions (MVOs) in ischemic stroke patients. RapidAI
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Timely detection of intracranial arterial occlusion is crucial for effective ischemic stroke reperfusion therapy.
- The study addresses the need for accurate and rapid diagnostic tools in acute stroke management.
- Comparing human expertise against AI software for occlusion detection is essential.
Purpose of the Study:
- To compare the diagnostic accuracy of neurologists and radiologists against RapidAI software for intracranial arterial occlusion detection.
- To evaluate the performance of RapidAI in identifying large-vessel occlusions (LVOs) and medium-vessel occlusions (MVOs).
- To assess the clinical utility of RapidAI in a real-world stroke setting.
Main Methods:
- A study involving 500 adult patients with suspected ischemic stroke who underwent multimodality imaging with RapidAI interpretation.
- Eight assessors (1 radiologist, 5 neurologists, 2 radiology trainees) evaluated scans.
- Diagnostic accuracy was assessed using positive and negative likelihood ratios (LR) compared to a reference standard panel of interventional neuroradiologists.
Main Results:
- RapidAI showed a positive LR of 8.49 for LVO detection and 5.0 for LVO or MVO detection.
- All readers exhibited higher sensitivity for LVO and LVO or MVO detection compared to RapidAI.
- Six out of eight readers demonstrated superior specificity to RapidAI for both LVO and LVO or MVO detection.
Conclusions:
- Experienced human readers achieve higher accuracy in identifying LVOs and MVOs than RapidAI software in clinical practice.
- The negative LR of RapidAI was insufficient to reliably rule out LVO or MVO.
- Human interpretation remains a critical component in the accurate diagnosis of intracranial occlusions in stroke patients.
Background:
Rapid detection of intracranial arterial occlusion in patients with ischemic stroke is important to facilitate timely reperfusion therapy. We compared the diagnostic accuracy of neurologists and radiologists against RapidAI (iSchema View, Menlo Park, CA) software for occlusion detection.
Methods:
Adult patients who presented to a single comprehensive stroke center over a 5-month interval with clinical suspicion of ischemic stroke and who underwent multimodality imaging with RapidAI interpretation were included. There were 8 assessors: 1 radiologist, 5 neurologists, and 2 radiology trainees. The reference standard was large-vessel occlusion (LVO) or medium-vessel occlusion (MVO) diagnosed by a panel of 4 interventional neuroradiologists. Positive likelihood ratio (LR) and negative LR were used to indicate how well readers correctly classified the presence of intracranial occlusions compared with the reference standard. The positive LR and negative LR for each reader were plotted on an LR graph using RapidAI LRs as comparator.
Results:
The assessors read scans from 500 patients (49.6% men). The positive LR of RapidAI for detection of LVO was 8.49 (95% CI, 5.75-12.54), and the negative LR was 0.41 (95% CI, 0.28-0.58). The positive LR for LVO or MVO for RapidAI was 5.0 (95% CI, 3.28-7.63), and the negative LR was 0.66 (95% CI, 0.56-0.79). Sensitivity for LVO (0.65-0.96) and for LVO or MVO (0.62-0.94) was higher for all readers compared with RapidAI (0.62 and 0.39, respectively). Six of 8 readers had superior specificity to RapidAI for LVO (0.75-0.98 versus 0.93) and LVO or MVO (0.55-0.95 versus 0.92).
Conclusions:
Experienced readers of acute stroke imaging can identify LVOs and MVOs with higher accuracy than RapidAI software in a real-world setting. The negative LR of RapidAI software was not sufficient to rule out LVO or MVO.
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