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We developed convolutional neural networks (CNNs) to detect free-floating anterior ethmoidal arteries (FFAEA), Onodi cells, and Haller cells on coronal sinus CT, and evaluated BioMedCLIP, a biomedical vision-language model (VLM), in a few-shot setting. CT scans from 122 ESS patients were anonymised, standardised coronal CT images were captured, and variant presence was annotated. Five ImageNet-pretrained CNN backbones were assessed using repeated patient-wise five-fold cross-validation across 40 configurations. The best CNNs achieved balanced accuracies of 77.8\u2009\u00b1\u20092.3% (FFAEA), 74.6\u2009\u00b1\u20092.5% (Onodi), and 63.7\u2009\u00b1\u20096.2% (Haller). BioMedCLIP achieved 65.5\u2009\u00b1\u20093.2%, 63.8\u2009\u00b1\u20091.8%, and 73.5\u2009\u00b1\u20093.6%, respectively, outperforming CNNs for Haller cell detection while providing competitive performance for the other variants. 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Informed consent was not required for this secondary analysis of fully anonymised, routinely acquired imaging data, with no patient contact and no impact on clinical care. All methods were carried out in accordance with relevant guidelines and regulations, including applicable UK data protection requirements.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"30235"}}