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In real-time screening, binary classification provides a straightforward first-line solution by differentiating normal and abnormal images, enabling prioritization of potentially abnormal cases for subsequent medical and disease-specific analysis. This study proposes RetinoMamba-AnatomyQuery, a lightweight model that integrates Vision Mamba-based global contextual representations, EfficientNet-B3-derived local features, anatomy\u2013lesion query refinement, and anatomy-aware feature fusion. The Ocular Disease Intelligent Recognition (ODIR)-2019 dataset and Brazilian Multilabel Ophthalmological Dataset (BRSET) are harmonized to construct the internal cohort, yielding 10,842 eligible unique patients following expert-assisted quality control and data curation. The proposed model achieves accuracy of 97.93%, recall of 97.77%, specificity of 98.07%, precision of 97.87%, and an F1-score of 97.82%, outperforming state-of-the-art models. Independent evaluation on the external dataset (Retinal Fundus Multi-disease Image Dataset (RFMiD)) reports an accuracy of 96.21% and an F1-score of 95.99%, demonstrating sustained performance across an independent data distribution. Computational evaluation demonstrates a controlled footprint of 23.9 million parameters and 8.3 giga floating-point operations, supporting computationally efficient screening. Overall, the findings demonstrate the effectiveness of anatomy-conditioned global\u2013local feature integration for reliable normal\/abnormal fundus discrimination.<\/jats:p>","DOI":"10.3390\/life16091577","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T14:12:09Z","timestamp":1789999929000},"page":"1577","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5445-7899","authenticated-orcid":false,"given":"Abdul Rahaman Wahab","family":"Sait","sequence":"first","affiliation":[{"name":"Department of Documents and Archive, Center of Documents and Administrative Communication, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2312-2807","authenticated-orcid":false,"given":"Yazeed","family":"Alkhurayyif","sequence":"additional","affiliation":[{"name":"Applied College, Shaqra University, Shaqra 11961, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Muchuchuti, S., and Viriri, S. 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