Journal article
Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke
Journal of stroke and cerebrovascular diseases, Vol.31(11), pp.106757-106757
09/09/2022
DOI: 10.1016/j.jstrokecerebrovasdis.2022.106757
PMID: 36099657
Abstract
OBJECTIVESAutomated image-level detection of large vessel occlusions (LVO) could expedite patient triage for mechanical thrombectomy. A few studies have previously attempted LVO detection using artificial intelligence (AI) on CT angiography (CTA) images. To our knowledge this is the first study to detect LVO existence and location on raw 4D-CTA/ CT perfusion (CTP) images using neural network (NN) models. MATERIALS AND METHODSRetrospective study using data from a level-I stroke center was performed. A total of 306 (187 with LVO, and 119 without) patients were evaluated. Image pre-processing included co-registration, normalization and skull stripping. Five consecutive time-points for each patient were selected to provide variable contrast density in data. Additional data augmentation included rotation and horizonal image flipping. Our model architecture consisted of two neural networks, first for classification (based on hemispheric asymmetry), followed by second model for exact site of LVO detection. Only cases deemed positive by the classification model were routed to the detection model, thereby reducing false positives and improving specificity. The results were compared with expert annotated LVO detection. RESULTSUsing a 80:20 split for training and validation, the combination of both classification and detection model achieved a sensitivity of 86.5%, a specificity of 89.5%, and an accuracy of 87.5%. A 5-fold cross-validation using the entire data achieved a mean sensitivity of 82.7%, a specificity of 89.8%, and an accuracy of 85.5% and a mean AUC of 0.89 (95% CI: 0.85-0.93). CONCLUSIONOur findings suggest that accurate image-level LVO detection is feasible on CTP raw images.
Details
- Title: Subtitle
- Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke
- Creators
- Girish BathlaDhruba DurjoySarv PriyaEdgar SamaniegoColin P Derdeyn
- Resource Type
- Journal article
- Publication Details
- Journal of stroke and cerebrovascular diseases, Vol.31(11), pp.106757-106757
- DOI
- 10.1016/j.jstrokecerebrovasdis.2022.106757
- PMID
- 36099657
- NLM abbreviation
- J Stroke Cerebrovasc Dis
- ISSN
- 1052-3057
- eISSN
- 1532-8511
- Publisher
- Elsevier Inc
- Language
- English
- Date published
- 09/09/2022
- Academic Unit
- Neurology; Radiology; Iowa Neuroscience Institute; Neurosurgery
- Record Identifier
- 9984297352102771
Metrics
6 Record Views