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However, increasing detection channels requires expensive optical components and\/or critically increases imaging time. To address this issue, we present an innovative combination of machine learning and optics as an integrated technology to compressively capture 12 nonconfocal channel AOSLO images simultaneously. Imaging of healthy participants and diseased subjects using the proposed deep compressed multichannel AOSLO showed enhanced visualization of rods, cones, and mural cells with over an order-of-magnitude improvement in imaging speed as compared to conventional offset aperture imaging. To facilitate the adaptation and integration with other in vivo microscopy systems, we made optical design, acquisition, and computational reconstruction codes open source.<\/jats:p>","DOI":"10.1126\/sciadv.adr5912","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T17:58:21Z","timestamp":1746813501000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":2,"title":["Deep compressed multichannel adaptive optics scanning light ophthalmoscope"],"prefix":"10.1126","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6322-2423","authenticated-orcid":true,"given":"Jongwan","family":"Park","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Duke University, Durham, NC, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristen","family":"Hagan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Duke University, 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