Self-supervised Text-vision Alignment for Automated Brain MRI Abnormality Detection: A Multicenter Study (ALIGN Study) Article Swipe
David Wood
,
Ayisha Al Busaidi
,
Ahmed Hammam
,
Siddharth Agarwal
,
Yiran Wei
,
Asif Mazumder
,
Gareth J. Barker
,
Peter Sasieni
,
A. Geetha
,
Robert A. Dineen
,
Permesh Singh Dhillon
,
Kavi Fatania
,
R. K. Nichols
,
Hilmar Spohr
,
Thomas C. Booth
,
Jeremy Macmullen-Price
,
Stuart Currie
,
Pirrone-Brusse Michael
,
Helen Estall
,
Maria Filyridou
,
Tharunniya Vamadevan
,
Carmen Dragos
,
Kanika Bhatia
,
Miguel Bertoni
,
Kanika Bhatia
,
Muthu Magesh
,
Sobha Xavier P
,
Maria Pantelidou
,
Gehad Abdalla
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1148/ryai.240619
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1148/ryai.240619
Purpose To develop a self-supervised text-vision framework to detect abnormalities on brain MRI scans by leveraging free-text neuroradiology reports, eliminating the need for expertlabeled training datasets. Materials and Methods This retrospective and prospective multicenter study included 81,936 brain MRI examinations and corresponding radiology reports for adult patients at two UK National Health Service (NHS) hospitals during January 2008-December 2019 for training and internal testing, and 1,369 prospectively collected examinations between March 2022-March 2024 from four separate NHS hospitals for external testing ( clinicaltrials.gov NCT043681). A neuroradiology language model (NeuroBERT) was trained using self-supervised tasks to generate report embeddings. Convolutional neural networks (one per MRI sequence) were trained to map scans to embeddings by minimizing mean squared error loss. The framework then detected abnormalities in new examinations by scoring scans against query sentences using textimage similarity. Model diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). Results The framework achieved an AUC of 0.95 (95% CI: 0.94, 0.97) for normal versus abnormal classification and generalized to external sites with examination-level AUCs of 0.90 (95% CI: 0.86, 0.93), 0.87 (95% CI: 0.83, 0.90), 0.86 (95% CI: 0.83, 0.90), and 0.85 (95% CI: 0.81, 0.89). In five zero-shot classification tasks—acute stroke, multiple sclerosis, intracranial hemorrhage, meningioma, and hydrocephalus—the framework achieved a mean AUC of 0.89 (range, 0.77–0.93). For visual-semantic image retrieval, mean precision was 0.84 among the top 15 images across seven pathologies. Conclusion The self-supervised text-vision framework accurately detected brain MRI abnormalities without expert-labeled datasets. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1148/ryai.240619
- OA Status
- hybrid
- OpenAlex ID
- https://openalex.org/W4416718099
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4416718099Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1148/ryai.240619Digital Object Identifier
- Title
-
Self-supervised Text-vision Alignment for Automated Brain MRI Abnormality Detection: A Multicenter Study (ALIGN Study)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-26Full publication date if available
- Authors
-
David Wood, Ayisha Al Busaidi, Ahmed Hammam, Siddharth Agarwal, Yiran Wei, Asif Mazumder, Gareth J. Barker, Peter Sasieni, A. Geetha, Robert A. Dineen, Permesh Singh Dhillon, Kavi Fatania, R. K. Nichols, Hilmar Spohr, Thomas C. Booth, Jeremy Macmullen-Price, Stuart Currie, Pirrone-Brusse Michael, Helen Estall, Maria Filyridou, Tharunniya Vamadevan, Carmen Dragos, Kanika Bhatia, Miguel Bertoni, Kanika Bhatia, Muthu Magesh, Sobha Xavier P, Maria Pantelidou, Gehad AbdallaList of authors in order
- Landing page
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https://doi.org/10.1148/ryai.240619Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1148/ryai.240619Direct OA link when available
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0Total citation count in OpenAlex
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