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To study static omega sign (visible full-blown \u03a9-shaped or vertical wrinkles at rest) and dynamic omega sign (newly appearing or deepening) with digital assessment for depression via deep learning, subjects with major depressive disorder (MDD) and controls were invited to take neutral photos and videotaped interviews to study the static and dynamic omega sign, respectively. A deep residual learning model was used to generate automatic prediction. In total, 252 MDD subjects (age: 50.9\u2009\u00b1\u200911.1 years; 66.7% female) and 192 age- and sex- controlled subjects (age: 49.6\u2009\u00b1\u200911.7\u2009y; 62.0% female) were recruited. The presence of static omega sign was marginally associated with an increased risk for MDD (MDD subjects: 24.3%, Controls: 16.0%; OR\u2009=\u20091.79, 95% CI\u2009=\u20090.99 to 3.44,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009=\u20090.06). In particular, comorbid MDD and generalized anxiety disorder (GAD) subjects had a significantly higher rate (45.5%) than controls (OR\u2009=\u20096.36, 95% CI\u2009=\u20091.71 to 23.63,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009=\u20090.006). The frequency of dynamic sign was associated with the presence of current moderate to severe depression (OR\u2009=\u20091.04, 95% CI\u2009=\u20091.01 to 1.07,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009=\u20090.02). In summary, MDD patients, especially comorbid with GAD, tended to have a higher rate of static omega sign than controls. The frequency of dynamic sign was associated with an increased risk of having moderate to severe depression. The advance of digital-AI technology offers a novel strategy to provide objective measurement of the omega sign that will assist clinical assessment of depression.\n                  <\/jats:p>","DOI":"10.1038\/s41398-026-04245-6","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T10:55:51Z","timestamp":1783421751000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Capturing omega sign in the clinical assessment of depression by deep learning"],"prefix":"10.1038","volume":"16","author":[{"given":"Jie","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Tung","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ngan Yin","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6545-0617","authenticated-orcid":false,"given":"Chris Xie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sijing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2692-2666","authenticated-orcid":false,"given":"Joey W. 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