{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T11:19:44Z","timestamp":1787570384456,"version":"build-2736575974"},"reference-count":81,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Oculomics uses ocular images, ocular biofluids and functional ocular tests to extract diagnostic information on local or systemic biological states. Most current evidence is cross-sectional: one retinal photograph, OCT scan, OCTA examination or tear sample is compared with a reference population, or converted into a diagnostic or prognostic score. This approach is useful for screening and risk enrichment, but it does not show whether the same patient is changing over time. In this review, dynamic oculomics, or oculomic kinetics, is presented as a diagnostic-monitoring approach based on repeated ocular measurements within the same individual. Its object is the personal trajectory: baseline level, short-term variability, long-term drift, response to stimulus, recovery and modification by treatment. We discuss the biological basis of ocular trajectories, the limits imposed by local ocular regulation and disease, and the technical constraints of fundus photography, structural OCT, OCTA, functional challenge tests, tear analysis, aqueous humour analysis, continuous intraocular pressure monitoring and home-based imaging. We also examine artificial intelligence, validation, governance, health-economic considerations and practical implementation. The available evidence does not support a universal ocular surrogate for systemic disease. More realistic applications include detection of unexpectedly rapid tissue change, individualised risk stratification, treatment-response assessment and recognition of discordance between systemic markers and ocular tissue behaviour.<\/jats:p>","DOI":"10.3390\/diagnostics16172699","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:07:53Z","timestamp":1787566073000},"page":"2699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Oculomics for Diagnostic Monitoring: Personal Ocular Trends, Risk Stratification and Treatment Response"],"prefix":"10.3390","volume":"16","author":[{"given":"Alessandro","family":"Avitabile","sequence":"first","affiliation":[{"name":"Biomedicine, Neuroscience and Advance Diagnostics (BIND) Department, University of Palermo, 90128 Palermo, Italy"},{"name":"Neurovisual Science Technology, Research Division (NEST-G.R.I.D.O.), 95123 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7152-2613","authenticated-orcid":false,"given":"Mario D.","family":"Toro","sequence":"additional","affiliation":[{"name":"Eye Clinic, Department of Public Health, University of Naples Federico II, 80131 Naples, Italy"},{"name":"Chair and Department of General and Paediatric Ophthalmology, Medical University of Lublin, 20-079 Lublin, Poland"},{"name":"Department of Special Surgery, University of Jordan, Queen Rania St., Amman 11942, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberta","family":"Amato","sequence":"additional","affiliation":[{"name":"Neurovisual Science Technology, Research Division (NEST-G.R.I.D.O.), 95123 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9577-2585","authenticated-orcid":false,"given":"Dario","family":"Rusciano","sequence":"additional","affiliation":[{"name":"Neurovisual Science Technology, Research Division (NEST-G.R.I.D.O.), 95123 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-0068","authenticated-orcid":false,"given":"Caterina","family":"Gagliano","sequence":"additional","affiliation":[{"name":"Department of Medicine and Surgery, Kore University of Enna, 94100 Enna, Italy"},{"name":"Ophthalmology Clinic, Mediterranean Foundation G.B. Morgagni, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning","volume":"2","author":"Poplin","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1136\/bjophthalmol-2021-319807","article-title":"Retinal age gap as a predictive biomarker for mortality risk","volume":"107","author":"Zhu","year":"2023","journal-title":"Br. J. 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