Prediction of the Fundus Tessellation Severity With Machine Learning Methods - Shao, Lei; Zhang, Xiaomei; Hu, Teng; Chen, Yang; Zhang, Chuan; Dong, Li; Ling, Saiguang; Dong, Zhou; Zhou, Wen Da; Zhang, Rui Heng; Qin, Lei; Wei, Wen Bin (Frontiers in Medicine, 2022)
Purpose: To predict the fundus tessellation (FT) severity with machine learning methods. Methods: A population-based cross-sectional study with 3468 individuals (mean age of 64.69.8 years) based on Beijing Eye Study 2011. Participants underwent detailed ophthalmic examinations including fundus images. Five machine learning methods including ordinal logistic regression, ordinal probit regression, ordinal log-gamma regression, ordinal forest and neural network were used. Main Outcome Measure: FT precision, recall, F1-score，weighted-average F1-score and AUC value Results: Observed from the in-sample fitting performance, the optimal model was ordinal forest, which had correct classification rate (precision) of 81.28%, while 34.75%, 93.73%, 70.03% and 24.82% in each classified group by FT severity. The AUC value was 0.7249. And the F1-score was 65.05%, weighted-average F1-score was 79.64% on the whole dataset. For out-of-sample prediction performance, the optimal model was ordinal logistic regression, which had precision of 77.12% on the validation dataset, while 19.57%, 92.68%, 64.74% and 6.76% in each classified group by FT severity. The AUC value was 0.7187. The classification accuracy of light FT group was the highest, while that of severe FT group was the lowest. And the F1-score was 54.46%, weighted-average F1-score was 74.19% on the whole dataset. Conclusions: The ordinal forest and ordinal logistic regression model have strong prediction performance. The threshold ranges of no FT and light, moderate, severe FT are [0,0.3078], (0.3078,0.3347], (0.3347,0.4048], (0.4048,1] and ≤ 3.7389, (3.7389,10.5053], (10.5053,13.9323], > 13.9323 respectively. These results can be applied to guide clinical fundus disease screening and FT severity assessment.