{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:00:20Z","timestamp":1777564820010,"version":"3.51.4"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop effective machine learning models that analyze pattern visual evoked potentials (PVEPs) to predict the stabilized visual acuity (VA) of patients with treated ocular trauma.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This experiment included 260 patients (220 males, average age 42.54 years) with unilateral ocular trauma. Four different machine learning algorithms, namely, support vector regression (SVR), Bayesian ridge (BYR), random forest regression (RFG), and extreme gradient boosting (XGBoost), were used to predict best corrected visual acuity (BCVA) values. Various ophthalmic parameters were input into the above algorithms for model training, and the performance of the algorithms was analyzed from the difference between the prediction value and the ground truth. Among the BCVA measured at least 6 months after injury was set as the ground truth. The best-performing model was further developed by tuning different parameter combinations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>All models achieved high diagnostic performance, with accuracy values ranging from 0.7875 to 0.8133. The XGBoost model predicted BCVA values with the lowest mean absolute error (MAE), at 0.1598 logarithm of the minimum angle of resolution (logMAR); the lowest root mean square error (RMSE), at 0.2402 logMAR; and the highest accuracy, at 0.8959.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Promising outcomes in BCVA prediction were achieved by the PVEP-trained machine learning models, which will be helpful in the clinical evaluation of patients after ocular trauma.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcell.2025.1619956","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T05:32:56Z","timestamp":1754026376000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models"],"prefix":"10.3389","volume":"13","author":[{"given":"Hongxia","family":"Hao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiemin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhilu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"8","DOI":"10.4103\/ijo.IJO_1848_19","article-title":"Artificial intelligence in ophthalmology and healthcare: an updated review of the techniques in use","volume":"69","author":"Bali","year":"2021","journal-title":"Indian J. Ophthalmol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"2475","DOI":"10.1007\/s10792-020-01426-0","article-title":"Evaluation of effects of positive airway pressure treatment on retinal fiber thickness and visual pathways using optic coherence tomography and visual evoked potentials in the patients with severe obstructive sleep apnea syndrome","volume":"40","author":"Batum","year":"2020","journal-title":"Int. Ophthalmol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.survophthal.2021.03.003","article-title":"Artificial intelligence: the unstoppable revolution in ophthalmology","volume":"67","author":"Benet","year":"2022","journal-title":"Surv. Ophthalmol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S13204-021-02208-5","article-title":"Google\u2019s new AI technology detects cardiac issues using retinal scan","volume":"13","author":"Borra","year":"2022","journal-title":"Appl. Nanosci."},{"key":"B5","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1007\/s00417-021-05511-7","article-title":"Analysis of international publication trends in artificial intelligence in ophthalmology","volume":"260","author":"Boudry","year":"2022","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"key":"B6","doi-asserted-by":"publisher","first-page":"e43","DOI":"10.1016\/j.ophtha.2022.01.002","article-title":"From data to deployment: the collaborative community on ophthalmic imaging roadmap for artificial intelligence in age-related macular degeneration","volume":"129","author":"Dow","year":"2022","journal-title":"Ophthalmology"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1016\/j.ophtha.2018.02.037","article-title":"A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography","volume":"125","author":"Grassmann","year":"2018","journal-title":"Ophthalmology"},{"key":"B8","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40662-021-00273-z","article-title":"Application of artificial intelligence in cataract management: current and future directions","volume":"9","author":"Gutierrez","year":"2022","journal-title":"Eye Vis. (Lond)."},{"key":"B9","doi-asserted-by":"publisher","first-page":"447","DOI":"10.12116\/j.issn.1004-5619.2023.230407","article-title":"Estimation of visual acuity after recovery from different degrees of ocular trauma using pattern visual evoked potential","volume":"40","author":"Hao","year":"2024","journal-title":"J. Forensic Med."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.3390\/jcm8101555","article-title":"Serial, visually-evoked potentials for the assessment of visual function in patients with craniosynostosis","volume":"8","author":"Haredy","year":"2019","journal-title":"J. Clin. Med."},{"key":"B11","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1016\/j.ophtha.2016.01.006","article-title":"Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050","volume":"123","author":"Holden","year":"2016","journal-title":"Ophthalmology"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1136\/bjophthalmol-2019-314860","article-title":"Prediction of visual outcomes by an artificial neural network following intravitreal injection and laser therapy for retinopathy of prematurity","volume":"104","author":"Huang","year":"2020","journal-title":"Br. J. Ophthalmol."},{"key":"B13","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/s0896-1549(02)00007-x","article-title":"The ocular trauma score (OTS)","volume":"15","author":"Kuhn","year":"2002","journal-title":"Ophthalmol. Clin. North Am."},{"key":"B14","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.3390\/diagnostics14192126","article-title":"Best-corrected visual acuity quantitative prediction for cataract patients: AI-assisted clinical diagnostics facilitation via the inverse problem algorithm","volume":"14","author":"Lin","year":"2024","journal-title":"Diagn. (Basel)"},{"key":"B15","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1167\/tvst.11.3.4","article-title":"Evaluation of an OCT-AI-Based telemedicine platform for retinal disease screening and referral in a primary care setting","volume":"11","author":"Liu","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1776","DOI":"10.1097\/SCS.0000000000004937","article-title":"Comparative evaluation of the prognostic value between the ocular trauma score and the pediatric penetrating ocular trauma score","volume":"29","author":"Morgan","year":"2018","journal-title":"J. Craniofac Surg."},{"key":"B17","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1016\/j.ophtha.2017.12.034","article-title":"Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration","volume":"125","author":"Rohm","year":"2018","journal-title":"Ophthalmology"},{"key":"B18","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1016\/j.survophthal.2021.08.004","article-title":"Artificial intelligence applications in different imaging modalities for corneal topography","volume":"67","author":"Shanthi","year":"2022","journal-title":"Surv. Ophthalmol."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.1136\/bjophthalmol-2019-315025","article-title":"Artificial intelligence-assisted telemedicine platform for cataract screening and management: a potential model of care for global eye health","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"B20","doi-asserted-by":"publisher","first-page":"1165135","DOI":"10.3389\/fmed.2023.1165135","article-title":"Prediction of postoperative visual acuity in patients with age-related cataracts using macular optical coherence tomography-based deep learning method","volume":"10","author":"Wang","year":"2023","journal-title":"Front. Med. (Lausanne)"},{"key":"B21","doi-asserted-by":"publisher","first-page":"652848","DOI":"10.3389\/fcell.2021.652848","article-title":"An optical coherence tomography-based deep learning algorithm for visual acuity prediction of highly myopic eyes after cataract surgery","volume":"9","author":"Wei","year":"2021","journal-title":"Front. Cell Dev. Biol."},{"key":"B22","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1186\/S12886-023-03079-W","article-title":"Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane","volume":"23","author":"Wen","year":"2023","journal-title":"BMC Ophthalmol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"842680","DOI":"10.3389\/fmed.2022.842680","article-title":"Predicting optical coherence tomography-derived high myopia grades from fundus photographs using deep learning","volume":"9","author":"Wu","year":"2022","journal-title":"Front. Med. (Lausanne)"},{"key":"B24","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1080\/02713683.2024.2430212","article-title":"Prediction of visual acuity after cataract surgery by deep learning methods using clinical information and color fundus photography","volume":"50","author":"Yang","year":"2025","journal-title":"Curr. Eye Res."},{"key":"B25","doi-asserted-by":"publisher","first-page":"5779210","DOI":"10.1155\/2022\/5779210","article-title":"Prediction of visual acuity after anti-VEGF therapy in diabetic macular edema by machine learning","volume":"2022","author":"Zhang","year":"2022","journal-title":"J. Diabetes Res."}],"container-title":["Frontiers in Cell and Developmental Biology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2025.1619956\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T05:32:57Z","timestamp":1754026377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2025.1619956\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"references-count":25,"alternative-id":["10.3389\/fcell.2025.1619956"],"URL":"https:\/\/doi.org\/10.3389\/fcell.2025.1619956","relation":{},"ISSN":["2296-634X"],"issn-type":[{"value":"2296-634X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,1]]},"article-number":"1619956"}}