Using Machine Learning to Forecast Visual Outcomes in Wet AMD
Ophthalmology, July 2018
Rohm et al. established a comprehensive data warehouse and applied machine-learning algorithms to predict visual acuity (VA) outcomes of patients who received 3 intravitreal injections for neovascular age-related macular degeneration. They were able to predict VA at 3 months, and results were comparable to actual measured VA (called ground truth in this study). Moreover, the study showed that the 3-month predictions of VA were more accurate than were the 12-month forecasts.
Five algorithms were used in the study (AdaBoost.R2, Lasso, Gradient Boosting, Random Forests, and Extremely Randomized Trees). Clinical data obtained from the data warehouse included VA measurements drawn from electronic health records and findings from optical coherence tomography. To provide a quality measure, both mean absolute error (MAE) and root mean square error (RMSE) were calculated for each algorithm. (RMSE penalizes outliers, allowing selection of the most robust algorithm.)
Three-month forecasts were made for 653 patients (738 eyes). Mean VA before the first injection of an anti–vascular endothelial growth factor (anti-VEGF) drug was 0.54 logMAR (±0.39). Of these patients, 456 (508 eyes) had sufficient follow-up data for the 12-month assessment. Among the 508 eyes, mean VA before the initial injection was 0.56 logMAR (±0.42). The main outcome measure was the difference in predicted versus ground-truth logMAR VA at months 3 and 12 after the start of anti-VEGF therapy.
Analyses showed that the MAE of predicted VA over ground truth was 0.11 logMAR (5.5 letters) for the 3-month prediction and 0.16 logMAR (8 letters) for the 12-month prediction. The 12-month RMSE was lowest (0.2 logMAR; 10 letters of change) if data from the 4 visits before the third injection were taken into account, but this was not demonstrated for the MAE. The best-performing algorithm was the Lasso L1 regularized linear model. Although 12-month forecasts were not as accurate as their 3-month counterparts, they may be helpful for encouraging patients to stay on their therapy, the authors said.
The original article can be found here.