{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T13:38:36Z","timestamp":1792589916775,"version":"4.5.0"},"reference-count":46,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"vor","delay-in-days":293,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004320","name":"Shiraz University of Medical Sciences","doi-asserted-by":"publisher","award":["29800"],"award-info":[{"award-number":["29800"]}],"id":[{"id":"10.13039\/501100004320","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Glaucoma is a progressive optic neuropathy in which longitudinal visual field (VF) prediction is essential for timely clinical decision\u2010making and preventing irreversible VF loss. However, accurate prediction remains challenging due to the noisy, high\u2010dimensional nature of VF data and their complex longitudinal patterns. Capturing these patterns may require increasingly sophisticated prediction models, but greater model complexity can reduce interpretability. Therefore, prediction models need to capture temporal patterns while balancing interpretability and computational feasibility. This study is aimed at evaluating and comparing the performance of four methods, including personalized models (first\u2010order autoregressive [AR(1)] and ordinary linear regression (OLR)), a population\u2010level long short\u2010term memory (LSTM), and a naive baseline (BL) forecast for glaucoma VF prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Data were simulated for patients with early\u2010stage glaucoma using a linear mixed effects model (LMM) to account for noise in VF measurements and the impact of individual effects on glaucoma progression. Future VFs were predicted from examinations 6\u201311 based on previous examinations. Prediction accuracy was assessed using root mean squared error (RMSE) and pointwise mean absolute error (PMAE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Evaluation of the four approaches showed that OLR and naive BL forecast achieved higher errors than AR(1) and LSTM models. LSTM performed best when the number of examinations was small (in the initial examinations, indeed) in slow, moderate, and fast progression rate scenarios. However, with a larger number of examinations, prediction errors increased for LSTM compared with AR(1), resulting in higher accuracy for AR(1), particularly under moderate and fast progression rate scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The performance of the models shows dependence on the number of examinations in the present study. These findings highlight the importance of temporal information in longitudinal VF prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/bmri\/2815308","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T12:41:51Z","timestamp":1792586511000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal Pointwise Visual Field Prediction in Early\u2010Stage Glaucoma Using Regression Models and Long Short\u2010Term Memory: A Simulation Study"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4964-7707","authenticated-orcid":false,"given":"Soheila","family":"Naderi","sequence":"first","affiliation":[{"name":"Department of Biostatistics School of Medicine Shiraz University of Medical Sciences  Shiraz Iran  sums.ac.ir"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8207-5916","authenticated-orcid":false,"given":"Eghbal","family":"Mansoori","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering Shiraz University  Shiraz Iran  shirazu.ac.ir"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2503-1922","authenticated-orcid":false,"given":"Masoumeh Sadat","family":"Masoumpour","sequence":"additional","affiliation":[{"name":"Poostchi Ophthalmology Research Center Department of Ophthalmology School of Medicine Shiraz University of Medical Sciences  Shiraz Iran  sums.ac.ir"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3073-2600","authenticated-orcid":false,"given":"Marziyeh","family":"Doostfatemeh","sequence":"additional","affiliation":[{"name":"Department of Biostatistics School of Medicine Shiraz University of Medical Sciences  Shiraz Iran  sums.ac.ir"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7366-1059","authenticated-orcid":false,"given":"Saeedeh","family":"Pourahmad","sequence":"additional","affiliation":[{"name":"Department of Biostatistics School of Medicine Shiraz University of Medical Sciences  Shiraz Iran  sums.ac.ir"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.lfs.2019.116907"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1076\/opep.11.2.67.28158"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2014.05.013"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.4443-07.2008"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.4103\/0301-4738.73694"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.11.2.16"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2018.11.004"},{"key":"e_1_2_12_8_2","first-page":"512","article-title":"The Use of Visual Field Indices in Detecting Changes in the Visual Field in Glaucoma","volume":"31","author":"Chauhan B. 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