Chen Shao-Chun, Chiu Hung-Wen, Chen Chun-Chen, Woung Lin-Chung, Lo Chung-Ming
Department of Ophthalmology, Taipei City Hospital, Taipei 10632, Taiwan.
J Clin Med. 2018 Nov 24;7(12):475. doi: 10.3390/jcm7120475.
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.
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.
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).
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.
人工神经网络（ANNs）是人工智能的一种类型。在此，我们使用基于人工神经网络的机器学习算法来自动预测雷珠单抗治疗糖尿病性黄斑水肿后的视力结果。
使用患者数据优化人工神经网络以进行回归计算。目标设定为52、78或104周时的最终视力。输入的基线变量包括性别、年龄、糖尿病类型或病情、全身性疾病、眼部状况和治疗时间表。随机设计三组来构建、测试和验证算法的准确性。
在52、78和104周时，分别纳入了512只、483只和464只眼。对于训练组、测试组和验证组，各自的相关系数分别为0.75、0.77和0.70（52周）；0.79、0.80和0.55（78周）；以及0.83、0.47和0.81（104周），而最终视力的平均标准误差分别为6.50、6.11和6.40（52周）；5.91、5.83和7.59（78周）；以及5.39、8.70和6.81（104周）。
仅根据基线特征，机器学习在预测雷珠单抗治疗预后方面具有良好的相关系数。这些模型可能是预测治疗成功的有用临床工具。
