13.12.2019 |
Mengyu Wang, PhD, Lucy Q. Shen, MD, Louis R. Pasquale, MD, Michael V. Boland, MD, PhD, Sarah R. Wellik, MD, Carlos Gustavo De Moraes, MD, Jonathan S. Myers, MD, Thao D. Nguyen, PhD, Robert Ritch, MD, Pradeep Ramulu, MD, PhD, Hui Wang, PhD, Jorryt Tichelaar, MS, Dian Li, MS, Peter J. Bex, PhD, Tobias Elze, PhD
בשל "הגנת זכויות יוצרים", מובא להלן קישור למאמר בלבד. לקריאתו בטקסט מלא, אנא פנה לספרייה הרפואית הזמינה לך.
Retrospective study - quantify the central visual field (VF) loss patterns in glaucoma using machine-learning.
8,712 patients with 13,951 Humphrey 10-2 tests from 13,951 eyes for cross-sectional analyses, and 824 patients with at least five reliable 10-2 tests at 6 or more month intervals from 1191 eyes for longitudinal analyses.
Total deviation values were used to determine the central VF patterns using the most recent 10-2 tests.
A 24-2 VF within a 3 month window of the 10-2 tests was used to stage eyes into mild, moderate or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline.
Archetypal analysis was applied to determine the central VF patterns.
Cross-validation was performed to determine the optimal number of patterns.
Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC).
American Academy of Ophthalmology, Volume 127, Issue 6, p731-738, June 2020