论文标题
通过时间意识的深度学习对青少年的近视预测
Myopia prediction for adolescents via time-aware deep learning
论文作者
论文摘要
背景：基于其可变的历史视觉记录，对青少年的球形等效物进行定量预测。 方法：从2019年10月到2022年3月，我们检查了来自中国成都6-20岁的37,586名青少年的双眼未矫正的视力，轴向长度，角膜曲率和轴向75,172眼。 80 \％样本由训练集和剩余的20 \％组成，以测试集形成。时间感知的长期短期记忆被用来定量预测青少年在两年半内的球形等效物。 结果：球形同等标准的测试集的平均绝对预测误差为0.273-0.257，如果我们考虑不同的历史记录和不同的预测持续时间，则从0.189-0.160到0.596-0.473。 结论：时间感知时间长的短期记忆被应用于不规则采样的时间序列中捕获的时间特征，这更符合真实数据的特征，因此具有更高的适用性，并有助于较早地识别近视的进展。总体误差0.273远小于临床上可接受预测的标准，例如0.75。
Background: Quantitative prediction of the adolescents' spherical equivalent based on their variable-length historical vision records. Methods: From October 2019 to March 2022, we examined binocular uncorrected visual acuity, axial length, corneal curvature, and axial of 75,172 eyes from 37,586 adolescents aged 6-20 years in Chengdu, China. 80\% samples consist of the training set and the remaining 20\% form the testing set. Time-Aware Long Short-Term Memory was used to quantitatively predict the adolescents' spherical equivalent within two and a half years. Result: The mean absolute prediction error on the testing set was 0.273-0.257 for spherical equivalent, ranging from 0.189-0.160 to 0.596-0.473 if we consider different lengths of historical records and different prediction durations. Conclusions: Time-Aware Long Short-Term Memory was applied to captured the temporal features in irregularly sampled time series, which is more in line with the characteristics of real data and thus has higher applicability, and helps to identify the progression of myopia earlier. The overall error 0.273 is much smaller than the criterion for clinically acceptable prediction, say 0.75.
