ArticleiScience2026
ROI-guided virtual narrow band imaging for laryngeal cancer screening.
Article in iScience, 2026. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
14 authors.
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Abstract
White light laryngoscopy is widely available but can miss subtle vascular changes associated with early laryngeal neoplasia. We developed a region-of-interest (ROI)-guided cycle-consistent generative adversarial network (CycleGAN) that translates white light imaging (WLI) images into virtual narrow band imaging (NBI) images, while emphasizing expert-annotated lesion regions. The framework was trained and evaluated on 775 weakly paired WLI-NBI image pairs and assessed with technical image quality metrics, subjective reader scoring, a visual Turing test, and a multi-center reader study involving 12 otolaryngologists from nine institutions. The model preserved lesion-region structure better than global background structure and improved overall reader accuracy compared with WLI alone (81.5% versus 73.4%). Assistance mainly increased sensitivity for junior readers and specificity for senior readers. These findings support virtual NBI as a potential software-based adjunct where optical NBI is unavailable, while requiring further external and small-lesion validation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.