ArticleJAMIA open2025
An open-source, automated machine learning approach for large-scale image retrieval for thoracic aorta analysis studies.
Article in JAMIA open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Objective: To develop an image retrieval pipeline capable of identifying specific series of thoracic aortic computed tomography (CT) scans from a diverse database. Materials and Methods: An automated image analysis pipeline was developed to select series that show the entire thoracic aorta with arterial phase contrast from within a heterogeneous institutional cohort of 4184 CT scans of the chest. Results: The automated pipeline identified 3435 (82%) studies from within the database that met criteria. Manual review confirmed 99.1% of the selected scans were accurately selected, and 93.6% of excluded scans were appropriately excluded. Discussion and Conclusion: We present an open-source, image-retrieval pipeline that, with a high degree of accuracy, can identify aortic imaging studies that meet specific criteria from within a heterogeneous collection of images. This pipeline serves as a framework that can be easily modified for other clinical use cases and can be deployed across multiple centers to promote multi-institutional research.
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