A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema

被引:19
作者
Chen, Shao-Chun [1 ]
Chiu, Hung-Wen [1 ]
Chen, Chun-Chen [1 ]
Woung, Lin-Chung [1 ]
Lo, Chung-Ming [1 ]
机构
[1] Taipei City Hosp, Dept Ophthalmol, Taipei 10632, Taiwan
关键词
artificial neural network; diabetic macular edema; machine learning; ranibizumab; ENDOTHELIAL GROWTH-FACTOR; DEFERRED LASER; PRACTICAL LESSONS; RETINOPATHY; PROMPT; TRIAMCINOLONE; MANAGEMENT; REGRESSION;
D O I
10.3390/jcm7120475
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
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页数:9
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