• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>JMIR Medical Informatics >Deep Learning–Based Prediction of Refractive Error Using Photorefraction Images Captured by a Smartphone: Model Development and Validation Study
【24h】

Deep Learning–Based Prediction of Refractive Error Using Photorefraction Images Captured by a Smartphone: Model Development and Validation Study

机译：使用智能手机捕获的光反弹图像基于深度学习的屈光误差预测：模型开发和验证研究

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background Accurately predicting refractive error in children is crucial for detecting amblyopia, which can lead to permanent visual impairment, but is potentially curable if detected early. Various tools have been adopted to more easily screen a large number of patients for amblyopia risk. Objective For efficient screening, easy access to screening tools and an accurate prediction algorithm are the most important factors. In this study, we developed an automated deep learning–based system to predict the range of refractive error in children (mean age 4.32 years, SD 1.87 years) using 305 eccentric photorefraction images captured with a smartphone. Methods Photorefraction images were divided into seven classes according to their spherical values as measured by cycloplegic refraction. Results The trained deep learning model had an overall accuracy of 81.6%, with the following accuracies for each refractive error class: 80.0% for ≤?5.0 diopters (D), 77.8% for ?5.0 D and ≤?3.0 D, 82.0% for ?3.0 D and ≤?0.5 D, 83.3% for ?0.5 D and +0.5 D, 82.8% for ≥+0.5 D and +3.0 D, 79.3% for ≥+3.0 D and +5.0 D, and 75.0% for ≥+5.0 D. These results indicate that our deep learning–based system performed sufficiently accurately. Conclusions This study demonstrated the potential of precise smartphone-based prediction systems for refractive error using deep learning and further yielded a robust collection of pediatric photorefraction images.
机译：背景技术准确预测儿童屈光误差对于检测弱视来说至关重要，这可能导致永久性视觉损伤，但如果早期检测到可能是可能的固化。已经采用了各种工具来更容易筛选大量弱视患者的弱视风险。客观有效筛选，轻松访问筛选工具和准确的预测算法是最重要的因素。在这项研究中，我们开发了一种自动化的深度学习系统，以预测使用用智能手机捕获的305偏心光反弹图像的儿童（平均年龄4.32岁，SD 1.87岁）的屈光误差范围。方法根据通过迅速折射测量的球形值，将光反弹图像分成七种类。结果培训的深度学习模型的整体精度为81.6％，每次屈光误差等级具有以下精度：80.0％≤≤≤ude屈光度（d），77.8％，77.8％>Δ5.0d和≤≤≤x≤x≤x≤x≤x≤x≤3.0d，82.0％对于>？3.0d和≤≤0.5d，Δ0.5d和<+ 0.5d，82.8％≥+ 0.5d和<+ 3.0d，79.3％，≥+ 3.0d和<+ 5.0d， ≥+ 5.0d的75.0％。这些结果表明，我们的深度学习的系统充分准确地进行。结论本研究证明了使用深度学习的屈光误差的基于精确的智能手机的预测系统的潜力，进一步产生了一种鲁棒的儿科光反弹图像。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号