Abstract
Purpose: Artificial neural networks (ANNs) are one type of artificial intelligence. Here, we use an ANN-based machine learning algorithm to automatically predict visual outcomes after ranibizumab treatment in diabetic macular edema. Methods: Patient data were used to optimize ANNs for regression calculation. The target was established as the final visual acuity at 52, 78, or 104 weeks. The input baseline variables were sex, age, diabetes type or condition, systemic diseases, eye status and treatment time tables. Three groups were randomly devised to build, test and demonstrate the accuracy of the algorithms. Results: At 52, 78 and 104 weeks, 512, 483 and 464 eyes were included, respectively. For the training group, testing group and validation group, the respective correlation coefficients were 0.75, 0.77 and 0.70 (52 weeks); 0.79, 0.80 and 0.55 (78 weeks); and 0.83, 0.47 and 0.81 (104 weeks), while the mean standard errors of final visual acuity were 6.50, 6.11 and 6.40 (52 weeks); 5.91, 5.83 and 7.59; (78 weeks); and 5.39, 8.70 and 6.81 (104 weeks). Conclusions: Machine learning had good correlation coefficients for predicating prognosis with ranibizumab with just baseline characteristics. These models could be the useful clinical tools for prediction of success of the treatments.
Highlights
Diabetic macular edema (DME) is a major complication of diabetic retinopathy
DME is associated with an ischemic change of retinal blood vessels that results in their release of a large volume of vascular endothelial growth factor (VEGF) [4,5]
Machine learning algorithms have been used for the prognosis of cancer, post-traumatic stress disorder, to detect brain pathology and to predict survival in patients with burns [26,27,28,29]; our study is the first to describe a novel machine learning algorithm that predicts visual outcomes in patients with diabetic macular edema who are treated with intravitreal ranibizumab
Summary
Diabetic macular edema (DME) is a major complication of diabetic retinopathy. The prevalence rate is 2–30% and increased risk in the population with poor diabetes control or longer diabetes years. According to the pathogenesis of DME, anti-VEGF agents, including aflibercept, bevacizumab and ranibizumab, are the most effective and safe choices for DME treatment. These medications can capture the VEGF agents, stop the serum or fluid leakage, reverse the thickening of macula and improve vision. Due to the complicated pathogenesis and multi-factors related prognosis, until now, there is still no a reliable prognostic factor to predict the final treatment outcome before any injection [10,11] In this case, if we can establish a method to predict final visual outcome and customized the treatment plan, we can achieve a great improvement in the quality of care in the patients of DME
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