Markus Rohm1, Volker Tresp2, Michael Müller3, Christoph Kern3, Ilja Manakov1, Maximilian Weiss3, Dawn A Sim4, Siegfried Priglinger3, Pearse A Keane4, Karsten Kortuem5. 1. Department of Ophthalmology, Ludwig-Maximilians-University Munich, Germany; Department of Computer Science, Ludwig-Maximilians-University Munich, Germany. 2. Department of Computer Science, Ludwig-Maximilians-University Munich, Germany. 3. Department of Ophthalmology, Ludwig-Maximilians-University Munich, Germany. 4. Moorfields Eye Hospital, London, United Kingdom. 5. Department of Ophthalmology, Ludwig-Maximilians-University Munich, Germany; Moorfields Eye Hospital, London, United Kingdom. Electronic address: karsten.kortuem@med.uni-muenchen.de.
Abstract
PURPOSE: To predict, by using machine learning, visual acuity (VA) at 3 and 12 months in patients with neovascular age-related macular degeneration (AMD) after initial upload of 3 anti-vascular endothelial growth factor (VEGF) injections. DESIGN: Database study. PARTICIPANTS: For the 3-month VA forecast, 653 patients (379 female) with 738 eyes and an average age of 74.1 years were included. The baseline VA before the first injection was 0.54 logarithm of the minimum angle of resolution (logMAR) (±0.39). A total of 456 of these patients (270 female, 508 eyes, average age: 74.2 years) had sufficient follow-up data to be included for a 12-month VA prediction. The baseline VA before the first injection was 0.56 logMAR (±0.42). METHODS: Five different machine-learning algorithms (AdaBoost.R2, Gradient Boosting, Random Forests, Extremely Randomized Trees, and Lasso) were used to predict VA in patients with neovascular AMD after treatment with 3 anti-VEGF injections. Clinical data features came from a data warehouse (DW) containing electronic medical records (41 features, e.g., VA) and measurement features from OCT (124 features, e.g., central retinal thickness). The VA of patient eyes excluded from machine learning was predicted and compared with the ground truth, namely, the actual VA of these patients as recorded in the DW. MAIN OUTCOME MEASURES: Difference in logMAR VA after 3 and 12 months upload phase between prediction and ground truth as defined. RESULTS: For the 3-month VA forecast, the difference between the prediction and ground truth was between 0.11 logMAR (5.5 letters) mean absolute error (MAE)/0.14 logMAR (7 letters) root mean square error (RMSE) and 0.18 logMAR (9 letters) MAE/0.2 logMAR (10 letters) RMSE. For the 12-month VA forecast, the difference between the prediction and ground truth was between 0.16 logMAR (8 letters) MAE/0.2 logMAR (10 letters) RMSE and 0.22 logMAR (11 letters) MAE/0.26 logMAR (13 letters) RMSE. The best performing algorithm was the Lasso protocol. CONCLUSIONS: Machine learning allowed VA to be predicted for 3 months with a comparable result to VA measurement reliability. For a forecast after 12 months of therapy, VA prediction may help to encourage patients adhering to intravitreal therapy.
PURPOSE: To predict, by using machine learning, visual acuity (VA) at 3 and 12 months in patients with neovascular age-related macular degeneration (AMD) after initial upload of 3 anti-vascular endothelial growth factor (VEGF) injections. DESIGN: Database study. PARTICIPANTS: For the 3-month VA forecast, 653 patients (379 female) with 738 eyes and an average age of 74.1 years were included. The baseline VA before the first injection was 0.54 logarithm of the minimum angle of resolution (logMAR) (±0.39). A total of 456 of these patients (270 female, 508 eyes, average age: 74.2 years) had sufficient follow-up data to be included for a 12-month VA prediction. The baseline VA before the first injection was 0.56 logMAR (±0.42). METHODS: Five different machine-learning algorithms (AdaBoost.R2, Gradient Boosting, Random Forests, Extremely Randomized Trees, and Lasso) were used to predict VA in patients with neovascular AMD after treatment with 3 anti-VEGF injections. Clinical data features came from a data warehouse (DW) containing electronic medical records (41 features, e.g., VA) and measurement features from OCT (124 features, e.g., central retinal thickness). The VA of patient eyes excluded from machine learning was predicted and compared with the ground truth, namely, the actual VA of these patients as recorded in the DW. MAIN OUTCOME MEASURES: Difference in logMAR VA after 3 and 12 months upload phase between prediction and ground truth as defined. RESULTS: For the 3-month VA forecast, the difference between the prediction and ground truth was between 0.11 logMAR (5.5 letters) mean absolute error (MAE)/0.14 logMAR (7 letters) root mean square error (RMSE) and 0.18 logMAR (9 letters) MAE/0.2 logMAR (10 letters) RMSE. For the 12-month VA forecast, the difference between the prediction and ground truth was between 0.16 logMAR (8 letters) MAE/0.2 logMAR (10 letters) RMSE and 0.22 logMAR (11 letters) MAE/0.26 logMAR (13 letters) RMSE. The best performing algorithm was the Lasso protocol. CONCLUSIONS: Machine learning allowed VA to be predicted for 3 months with a comparable result to VA measurement reliability. For a forecast after 12 months of therapy, VA prediction may help to encourage patients adhering to intravitreal therapy.
Authors: Maximilian Pfau; Guenther Walther; Leon von der Emde; Philipp Berens; Livia Faes; Monika Fleckenstein; Tjebo F C Heeren; Karsten Kortüm; Sandrine H Künzel; Philipp L Müller; Peter M Maloca; Sebastian M Waldstein; Maximilian W M Wintergerst; Steffen Schmitz-Valckenberg; Robert P Finger; Frank G Holz Journal: Ophthalmologe Date: 2020-10 Impact factor: 1.059
Authors: Henrik Faatz; Marie-Louise Gunnemann; Kai Rothaus; Marius Book; Matthias Gutfleisch; Albrecht Lommatzsch; Daniel Pauleikhoff Journal: Ophthalmologe Date: 2021-02 Impact factor: 1.059
Authors: Maximilian Pfau; Soumya Sahu; Rawan Allozi Rupnow; Kathleen Romond; Desiree Millet; Frank G Holz; Steffen Schmitz-Valckenberg; Monika Fleckenstein; Jennifer I Lim; Luis de Sisternes; Theodore Leng; Daniel L Rubin; Joelle A Hallak Journal: Transl Vis Sci Technol Date: 2021-06-01 Impact factor: 3.283