ArticleNature communications2022
Emergency triage of brain computed tomography via anomaly detection with a deep generative model.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 30 citations in OpenAlex.
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- Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges.Current atherosclerosis reports · 2025Review
- Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging.Nature communications · 2025Article
- Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.Korean journal of radiology · 2024Review
- Insights and Considerations in Development and Performance Evaluation of Generative Adversarial Networks (GANs): What Radiologists Need to Know.Diagnostics (Basel, Switzerland) · 2024Review
- Generative adversarial network-based reconstruction of healthy anatomy for anomaly detection in brain CT scans.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.Korean journal of radiology · 2024Review
- Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning.Korean journal of radiology · 2023Review
- Unsupervised anomaly detection with generative adversarial networks in mammography.Scientific reports · 2023Article
- Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection.Frontiers in radiology · 2023Article
- Generative AI for brain image computing and brain network computing: a review.Frontiers in neuroscience · 2023Review
- Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases.Biosensors · 2022Review
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Triage is essential for the early diagnosis and reporting of neurologic emergencies. Herein, we report the development of an anomaly detection algorithm (ADA) with a deep generative model trained on brain computed tomography (CT) images of healthy individuals that reprioritizes radiology worklists and provides lesion attention maps for brain CT images with critical findings. In the internal and external validation datasets, the ADA achieved area under the curve values (95% confidence interval) of 0.85 (0.81-0.89) and 0.87 (0.85-0.89), respectively, for detecting emergency cases. In a clinical simulation test of an emergency cohort, the median wait time was significantly shorter post-ADA triage than pre-ADA triage by 294 s (422.5 s [interquartile range, IQR 299] to 70.5 s [IQR 168]), and the median radiology report turnaround time was significantly faster post-ADA triage than pre-ADA triage by 297.5 s (445.0 s [IQR 298] to 88.5 s [IQR 179]) (all p < 0.001).
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