Multilevel Multimodal Framework for Automatic Collateral Scoring in Brain Stroke Article Swipe
Rishi Raj
,
P. Dayananda
,
Ayush Gupta
,
Jimson Mathew
,
Santhosh Kumar Kannath
,
Adity Prakash
,
Jeny Rajan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3368504
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3368504
In patients with ischemic brain stroke, collateral circulation plays a crucial role in selecting patients suitable for endovascular therapy. The presence of well-developed collaterals improves the patient’s chances of recovery. In clinical practice, the presence of collaterals is diagnosed on a Computed Tomography Angiography scan. The radiologist grades it on the basis of subjective visual assessment, which is prone to interobserver and intraobserver variability. Computer-based methods of collateral assessment face the challenge of non-uniform scan volume, leading to manual selection of slices, meaning that the most imperative slices have to be manually selected by the radiologist. This paper proposes a multilevel multimodal hierarchical framework for automated collateral scoring. Specifically, we propose deploying a Convolutional Neural Network for image selection based on the visibility of collaterals and a multimodal model for comparing the occluded and contralateral sides of the brain for collateral scoring. We also generate a patient-level prediction by integrating automated machine learning in the proposed framework. While the proposed multimodal predictor contributes to Artificial Intelligence, the proposed end-to-end framework is an application in engineering. The proposed framework has been trained and tested on 116 patients, with five-fold cross-validation, achieving an accuracy of 91.17% for multi-class collateral scores and 94.118% for binary class collateral scores. The proposed multimodal predictor achieved a weighted F1 score of 0.86 and 0.95 on multi-class and binary-class collateral scores, respectively. The proposed framework is fast, efficient, and scalable for real-world deployments. Automated evaluation of collaterals with attention maps for explainability would complement radiologists’ efforts. Code for the proposed framework is available at: https://github.com/rishiraj-cs/collaterals_ML_MM.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3368504
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10443377.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392007260
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392007260Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3368504Digital Object Identifier
- Title
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Multilevel Multimodal Framework for Automatic Collateral Scoring in Brain StrokeWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Rishi Raj, P. Dayananda, Ayush Gupta, Jimson Mathew, Santhosh Kumar Kannath, Adity Prakash, Jeny RajanList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3368504Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10443377.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10443377.pdfDirect OA link when available
- Concepts
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Collateral, Convolutional neural network, Computer science, Artificial intelligence, Collateral circulation, Class (philosophy), Machine learning, Radiology, Medicine, Finance, EconomicsTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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