Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone
Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone
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DOI：
10.1136/bjophthalmol-2020-318107
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发表时间：
2021-07-14
影响因子：
4.1
通讯作者：
Murata, Hiroshi
中科院分区：
文献类型：
--
作者：
Nakahara, Kenichi;Asaoka, Ryo;Murata, Hiroshi
背景/目的是验证一种深度学习算法，以根据智能手机获得的眼底照片来诊断青光眼。方法使用普通眼底相机获取训练数据集，包括1364张具有青光眼适应症的彩色眼底照片和1768张无青光眼特征的彩色眼底照片。测试数据集包括73例青光眼患者的73只眼和正常者的眼。在测试数据集中，使用普通眼底相机和智能手机获取眼底照片。开发了一种深度学习算法，用于使用训练数据集诊断青光眼。训练好的神经网络通过使用普通眼底相机和智能手机的图像在测试数据集上对青光眼或正常的诊断预测结果进行评估。用受试者工作特征曲线下面积(AROC)评估诊断的准确性。结果使用眼底相机的AROC为98.9%，使用智能手机的AROC为84.2%。当仅在晚期青光眼(平均偏差为-12分贝，N=26)进行验证时，使用眼底相机的AROC为99.3%，使用智能手机的AROC为90.0%。在使用不同相机时，这些AROC值有显著差异。结论深度学习算法在从基于智能手机的眼底照片中自动筛查青光眼的有效性得到了验证。该算法具有相当高的诊断能力，特别是在患有晚期青光眼的眼睛。
Background/aims To validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone. Methods A training dataset consisting of 1364 colour fundus photographs with glaucomatous indications and 1768 colour fundus photographs without glaucomatous features was obtained using an ordinary fundus camera. The testing dataset consisted of 73 eyes of 73 patients with glaucoma and 89 eyes of 89 normative subjects. In the testing dataset, fundus photographs were acquired using an ordinary fundus camera and a smartphone. A deep learning algorithm was developed to diagnose glaucoma using a training dataset. The trained neural network was evaluated by prediction result of the diagnostic of glaucoma or normal over the test datasets, using images from both an ordinary fundus camera and a smartphone. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC). Results The AROC with a fundus camera was 98.9% and 84.2% with a smartphone. When validated only in eyes with advanced glaucoma (mean deviation value < -12 dB, N=26), the AROC with a fundus camera was 99.3% and 90.0% with a smartphone. There were significant differences between these AROC values using different cameras. Conclusion The usefulness of a deep learning algorithm to automatically screen for glaucoma from smartphone-based fundus photographs was validated. The algorithm had a considerable high diagnostic ability, particularly in eyes with advanced glaucoma.