International journal of ophthalmology · 2026
Visual field prediction using K-means clustering in patients with primary open angle glaucoma.
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Lee Junyoung, Kim Jihun, Kim Hwayoung, Moon Sangwoo, Kim EunAh, Jeong Sanghun, Yang Hojin, Lee Jiwoong
Аннотация
AIM: To evaluate long-term visual field (VF) prediction using K-means clustering in patients with primary open angle glaucoma (POAG).
METHODS: Patients 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 10th VF test. Three to nine VF tests were used to predict the 10th VF test, and the prediction errors (root mean square error, RMSE) of each clustering method and pointwise linear regression (PLR) were compared.
RESULTS: The 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 n=1:3 and 1:4 (both P≤0.003). The prediction errors of K-means clustering were lower than those of HOPACH in all sections (n=1:4 to 1:9; all P≤0.011), except for n=1:3 (P=0.680). PLR outperformed K-means clustering only when n=1:8 and 1:9 (both P≤0.020).
CONCLUSION: K-means clustering can predict long-term VF test results more accurately in patients with POAG with limited VF data.
METHODS: Patients 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 10th VF test. Three to nine VF tests were used to predict the 10th VF test, and the prediction errors (root mean square error, RMSE) of each clustering method and pointwise linear regression (PLR) were compared.
RESULTS: The 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 n=1:3 and 1:4 (both P≤0.003). The prediction errors of K-means clustering were lower than those of HOPACH in all sections (n=1:4 to 1:9; all P≤0.011), except for n=1:3 (P=0.680). PLR outperformed K-means clustering only when n=1:8 and 1:9 (both P≤0.020).
CONCLUSION: K-means clustering can predict long-term VF test results more accurately in patients with POAG with limited VF data.