Jinho Lee1,2, Young Kook Kim1,2, Jin Wook Jeoung1,2, Ahnul Ha1,2, Yong Woo Kim1,2, Ki Ho Park3,4. 1. Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea. 2. Department of Ophthalmology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea. 3. Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea. kihopark@snu.ac.kr. 4. Department of Ophthalmology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea. kihopark@snu.ac.kr.
Abstract
PURPOSE: To assess the performance of machine learning classifiers for prediction of progression of normal-tension glaucoma (NTG) in young myopic patients. STUDY DESIGN: Cross-sectional study. METHODS: One hundred and fifty-five eyes of 155 myopic NTG patients (axial length [AL] ≥ 24.00 mm and refractive error ≤ - 3.0 D) between the ages of 20 and 40 were enrolled and divided into training (110) and test (45) sets. Sixty-five eyes showed glaucoma progression as defined by standard automated perimetry, while 91 eyes (nonprogressors) had been stable over the course of a follow-up period of at least 3 years. Two machine learning classifiers were built using the random forest and extremely randomized trees (extra-trees) models. Baseline clinical measurements obtained only at the initial visit were used as input features. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the accuracy of prediction. RESULTS: Mean age and AL did not significantly differ between the 2 groups on either the training or the test set. The extra-trees model achieved an AUC of 0.881 [95% CI 0.814-0.945], higher than that of the random forest model (0.811 [0.731-0.888]; P = 0.010). The extra-trees model also outperformed all the clinical measurements for prediction of NTG progression, including average macular ganglion cell-inner plexiform layer thickness (0.735 [0.639-0.831]) and average circumpapillary retinal nerve fiber layer thickness (0.691 [0.590-0.792]; both P < 0.001). CONCLUSIONS: In young myopic patients, the machine learning classifier with the extra-trees model can predict glaucomatous progression more effectively than clinical diagnostic parameters.
PURPOSE: To assess the performance of machine learning classifiers for prediction of progression of normal-tension glaucoma (NTG) in young myopic patients. STUDY DESIGN: Cross-sectional study. METHODS: One hundred and fifty-five eyes of 155 myopic NTG patients (axial length [AL] ≥ 24.00 mm and refractive error ≤ - 3.0 D) between the ages of 20 and 40 were enrolled and divided into training (110) and test (45) sets. Sixty-five eyes showed glaucoma progression as defined by standard automated perimetry, while 91 eyes (nonprogressors) had been stable over the course of a follow-up period of at least 3 years. Two machine learning classifiers were built using the random forest and extremely randomized trees (extra-trees) models. Baseline clinical measurements obtained only at the initial visit were used as input features. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the accuracy of prediction. RESULTS: Mean age and AL did not significantly differ between the 2 groups on either the training or the test set. The extra-trees model achieved an AUC of 0.881 [95% CI 0.814-0.945], higher than that of the random forest model (0.811 [0.731-0.888]; P = 0.010). The extra-trees model also outperformed all the clinical measurements for prediction of NTG progression, including average macular ganglion cell-inner plexiform layer thickness (0.735 [0.639-0.831]) and average circumpapillary retinal nerve fiber layer thickness (0.691 [0.590-0.792]; both P < 0.001). CONCLUSIONS: In young myopic patients, the machine learning classifier with the extra-trees model can predict glaucomatous progression more effectively than clinical diagnostic parameters.
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