{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T05:26:44Z","timestamp":1786685204993,"version":"build-2736575974"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01AG073410"],"award-info":[{"award-number":["R01AG073410"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01HL161253"],"award-info":[{"award-number":["R01HL161253"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01NS126282"],"award-info":[{"award-number":["R01NS126282"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01AG073598"],"award-info":[{"award-number":["R01AG073598"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01NS131347"],"award-info":[{"award-number":["R01NS131347"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01NS130119"],"award-info":[{"award-number":["R01NS130119"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Study Objectives<\/jats:title>\n                    <jats:p>Manual sleep staging in pediatric populations is challenging due to developmental variability and limited scoring consistency, especially in infants and toddlers. We developed a multimodal deep learning model for pediatric sleep staging and evaluated its performance across a broad age range and diverse clinical subgroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We trained a U-Net-inspired encoder-decoder model (pediatric SleepNet) using 9-channel input signals: electroencephalography (EEG), electrooculography (EOG), and chin electromyography (EMG) using 35-epoch segments from clinical pediatric polysomnograms (PSGs). Models were trained separately on three age groups (&amp;lt;6\u00a0months, 6-12\u00a0months, &amp;gt;1\u00a0year) using 9150 PSGs, with 2455 PSGs reserved for validation. Evaluation was conducted on 3804 held-out test recordings. Performance was compared with U-Sleep and the Complete Artificial Intelligence Sleep Report (CAISR), and stratified analyses were performed across ages, sexes, and seven ICD-10-based disease categories. External validation was conducted on two independent datasets, CHAT and PATS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>pediatric SleepNet achieved robust performance across all age groups, with mean Cohen\u2019s Kappa increasing from 0.49 (0\u20136\u00a0months) to 0.72 (&amp;gt;12\u00a0years). It significantly outperformed U-Sleep and CAISR across early developmental stages. Three-class staging yielded mean Cohen\u2019s Kappa increasing from 0.66 (0\u20136\u00a0months) to 0.79 (&amp;gt;12\u00a0years). Sex-based differences were negligible. However, significant reductions in performance were observed in children with epilepsy, Down syndrome, hydrocephalus, and other neurodevelopmental conditions. External validation yielded Kappa values &amp;gt;0.69 comparable to the internal test set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>pediatric SleepNet demonstrates reliable sleep staging across pediatric development. Its robust performance across age, disease, and external datasets supports its potential for clinical and research use in pediatric sleep medicine.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/sleep\/zsag064","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T12:34:27Z","timestamp":1772541267000},"source":"Crossref","is-referenced-by-count":2,"title":["Pediatric SleepNet: a deep learning network for reliable pediatric sleep staging across developmental stages"],"prefix":"10.1093","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7944-2260","authenticated-orcid":false,"given":"Ayush","family":"Tripathi","sequence":"first","affiliation":[{"name":"Department of Neurology, Beth Israel Deaconess Medical Center , Boston, MA ,","place":["United States"]},{"name":"Harvard Medical School , Boston, MA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4818-2130","authenticated-orcid":false,"given":"Arnav","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Neurology, Beth Israel Deaconess Medical Center , Boston, MA ,","place":["United States"]},{"name":"Harvard John A. 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