Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images
المؤلفون المشاركون
Nakazawa, Toru
Shiga, Yukihiro
Omodaka, Kazuko
Akiba, Masahiro
An, Guangzhou
Hashimoto, Kazuki
Tsuda, Satoru
Takada, Naoko
Kikawa, Tsutomu
Yokota, Hideo
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-02-18
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images.
In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon).
Thickness and deviation maps were created with a segmentation algorithm.
Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map.
Data augmentation and dropout were performed to train the CNN.
For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer.
The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models.
The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps.
The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963.
Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images.
This system should help to improve the diagnostic accuracy in glaucoma.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
An, Guangzhou& Omodaka, Kazuko& Hashimoto, Kazuki& Tsuda, Satoru& Shiga, Yukihiro& Takada, Naoko…[et al.]. 2019. Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1175174
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
An, Guangzhou…[et al.]. Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images. Journal of Healthcare Engineering No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1175174
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
An, Guangzhou& Omodaka, Kazuko& Hashimoto, Kazuki& Tsuda, Satoru& Shiga, Yukihiro& Takada, Naoko…[et al.]. Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1175174
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1175174
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر