The British journal of ophthalmology · 2019
Prediction of visual outcomes by an artificial neural network following intravitreal injection and laser therapy for retinopathy of prematurity.
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Huang Ching-Yen, Kuo Ren-Jieh, Li Cheng-Han, Ting Daniel S, Kang Eugene Yu-Chuan, Lai Chi-Chun, Tseng Hsiao-Jung, Yang Lan-Yan, Wu Wei-Chi
Аннотация
AIMS: To construct a program to predict the visual acuity (VA), best corrected VA (BCVA) and spherical equivalent (SE) of patients with retinopathy of prematurity (ROP) from 3 to 12 years old after intravitreal injection (IVI) of anti-vascular endothelial growth factor and/or laser photocoagulation treatment.
METHODS: This retrospective study employed a feedforward artificial neural network with an error backpropagation learning algorithm to predict visual outcomes based on patient birth data, treatment received and age at follow-up. Patients were divided into two groups based on prior treatments. The main outcome measures were the difference between the predicted and actual values of visual outcomes. These were analysed using the normalised root mean square error (RMSE). Two-way repeated measures analysis of variance was used to compare the predictive accuracy by this algorithm.
RESULTS: A total of 60 ROP infants with prior treatments were included. In the IVI group, the normalised average RMSE for VA, BCVA, and SE was 0.272, 0.185 and 0.131, respectively. In the laser group, the normalised average RMSE for VA, BCVA and SE was 0.190, 0.250 and 0.104, respectively. This result shows that better predictive power was obtained for SE than for VA or BCVA in both the IVI and laser groups (p<0.001). In addition, the algorithm performed slightly better in predicting visual outcomes in the laser group (p<0.001).
CONCLUSIONS: This algorithm offers acceptable power for predicting visual outcomes in patients with ROP with prior treatment. Predictions of SE were more precise than predictions of for VA and BCVA in both groups.
METHODS: This retrospective study employed a feedforward artificial neural network with an error backpropagation learning algorithm to predict visual outcomes based on patient birth data, treatment received and age at follow-up. Patients were divided into two groups based on prior treatments. The main outcome measures were the difference between the predicted and actual values of visual outcomes. These were analysed using the normalised root mean square error (RMSE). Two-way repeated measures analysis of variance was used to compare the predictive accuracy by this algorithm.
RESULTS: A total of 60 ROP infants with prior treatments were included. In the IVI group, the normalised average RMSE for VA, BCVA, and SE was 0.272, 0.185 and 0.131, respectively. In the laser group, the normalised average RMSE for VA, BCVA and SE was 0.190, 0.250 and 0.104, respectively. This result shows that better predictive power was obtained for SE than for VA or BCVA in both the IVI and laser groups (p<0.001). In addition, the algorithm performed slightly better in predicting visual outcomes in the laser group (p<0.001).
CONCLUSIONS: This algorithm offers acceptable power for predicting visual outcomes in patients with ROP with prior treatment. Predictions of SE were more precise than predictions of for VA and BCVA in both groups.