Evidence map›Paper›PMID 41524021›Full record

ArticleInternational journal of ophthalmology2026

Visual field prediction using K-means clustering in patients with primary open angle glaucoma.

Junyoung Lee, Jihun Kim, Hwayoung Kim, Sangwoo Moon, EunAh Kim, Sanghun Jeong, Hojin Yang, Jiwoong Lee

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Article in International journal of ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Junyoung LeeDepartment of Ophthalmology, Pusan National University College of Medicine, Busan 49241, Republic of Korea.
Jihun KimJunggeun Eye Hospital, Busan 47257, Republic of Korea.
Hwayoung KimDepartment of Ophthalmology, Pusan National University College of Medicine, Busan 49241, Republic of Korea.
Sangwoo MoonDepartment of Ophthalmology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea.
EunAh KimDepartment of Ophthalmology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Republic of Korea.
Sanghun JeongDepartment of Statistics, Pusan National University, Busan 46241, Republic of Korea.
Hojin YangDepartment of Statistics, Pusan National University, Busan 46241, Republic of Korea.
Jiwoong LeeDepartment of Ophthalmology, Pusan National University College of Medicine, Busan 49241, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo evaluate long-term visual field (VF) prediction using K-means clustering in patients with primary open angle glaucoma (POAG).

methodsPatients who underwent 24-2 VF tests ≥10 were included in this study. Using 52 total deviation values (TDVs) from the first 10 VF tests of the training dataset, VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid (HOPACH) and K-means clustering. Based on the clustering results, a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10

resultsThe training group consisted of 228 patients (mean age, 54.20±14.38y; 123 males and 105 females), and the testing group included 81 patients (mean age, 54.88±15.22y; 43 males and 38 females). All subjects were diagnosed with POAG. Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering, respectively. K-means clustering had a lower prediction error than PLR when

conclusionK-means clustering can predict long-term VF test results more accurately in patients with POAG with limited VF data.

Indexed as

hierarchical ordered partitioning and collapsing hybridK-means clusteringpointwise linear regressionvisual field prediction

Identifiers

PMID41524021
PMCPMC12782057

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.