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We aimed to develop a deep learning model to predict A\u03b2 cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 A\u03b2 PET images and CSF measurements to train and validate a convolutional neural network (\u201cArcheD\u201d). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical A\u03b2. We also compared the brain region's relevance for the model's CSF prediction within clinical\u2010based and biological\u2010based classifications. ArcheD\u2010predicted A\u03b2 CSF values correlated with measured A\u03b2 CSF values (r\u2009=\u20090.92; q\u2009&lt;\u20090.01), SUVR (r\n                    <jats:sub>AV45<\/jats:sub>\n                    \u2009=\u2009\u22120.64, r\n                    <jats:sub>FBB<\/jats:sub>\n                    \u2009=\u2009\u22120.69; q\u2009&lt;\u20090.01) and episodic memory measures (0.33\u2009&lt;\u2009r\u2009&lt;\u20090.44; q\u2009&lt;\u20090.01). For both classifications, cerebral white matter significantly contributed to CSF prediction (\n                    <jats:italic>q<\/jats:italic>\n                    \u2009&lt;\u20090.01), specifically in non\u2010symptomatic and early stages of AD. However, in late\u2010stage disease, the brain stem, subcortical areas, cortical lobes, limbic lobe and basal forebrain made more significant contributions (q\u2009&lt;\u20090.01). Considering cortical grey matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted A\u03b2 CSF concentration from A\u03b2 PET scans, offering potential clinical utility for A\u03b2 level determination.\n                  <\/jats:p>","DOI":"10.1111\/ejn.16332","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T01:43:51Z","timestamp":1712281431000},"page":"3030-3044","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid\u2010beta from amyloid PET images"],"prefix":"10.1111","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2906-6279","authenticated-orcid":false,"given":"Arina A.","family":"Tagmazian","sequence":"first","affiliation":[{"name":"Institute for Molecular Medicine Finland (FIMM), HiLIFE University of Helsinki  Helsinki Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3234-912X","authenticated-orcid":false,"given":"Claudia","family":"Schwarz","sequence":"additional","affiliation":[{"name":"Institute for Molecular Medicine Finland (FIMM), HiLIFE University of Helsinki  Helsinki Finland"},{"name":"Department of Neurology University Medicine Greifswald  Greifswald Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6745-8441","authenticated-orcid":false,"given":"Catharina","family":"Lange","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine Charit\u00e9 \u2010 Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin  Berlin Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9818-6370","authenticated-orcid":false,"given":"Esa","family":"Pitk\u00e4nen","sequence":"additional","affiliation":[{"name":"Institute for Molecular Medicine Finland (FIMM), HiLIFE University of Helsinki  Helsinki Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6534-3667","authenticated-orcid":false,"given":"Eero","family":"Vuoksimaa","sequence":"additional","affiliation":[{"name":"Institute for Molecular Medicine Finland (FIMM), HiLIFE University of Helsinki  Helsinki Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the Alzheimer's Disease Neuroimaging 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