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Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images
المؤلفون المشاركون
Ogura, Yuichiro
Kuwayama, Soichiro
Ayatsuka, Yuji
Yanagisono, Daisuke
Uta, Takaki
Usui, Hideaki
Kato, Aki
Takase, Noriaki
Yasukawa, Tsutomu
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-7، 7ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-04-09
دولة النشر
مصر
عدد الصفحات
7
التخصصات الرئيسية
الملخص EN
Purpose.
Although optical coherence tomography (OCT) is essential for ophthalmologists, reading of findings requires expertise.
The purpose of this study is to test deep learning with image augmentation for automated detection of chorioretinal diseases.
Methods.
A retina specialist diagnosed 1,200 OCT images.
The diagnoses involved normal eyes (n=570) and those with wet age-related macular degeneration (AMD) (n=136), diabetic retinopathy (DR) (n=104), epiretinal membranes (ERMs) (n=90), and another 19 diseases.
Among them, 1,100 images were used for deep learning training, augmented to 59,400 by horizontal flipping, rotation, and translation.
The remaining 100 images were used to evaluate the trained convolutional neural network (CNN) model.
Results.
Automated disease detection showed that the first candidate disease corresponded to the doctor’s decision in 83 (83%) images and the second candidate disease in seven (7%) images.
The precision and recall of the CNN model were 0.85 and 0.97 for normal eyes, 1.00 and 0.77 for wet AMD, 0.78 and 1.00 for DR, and 0.75 and 0.75 for ERMs, respectively.
Some of rare diseases such as Vogt–Koyanagi–Harada disease were correctly detected by image augmentation in the CNN training.
Conclusion.
Automated detection of macular diseases from OCT images might be feasible using the CNN model.
Image augmentation might be effective to compensate for a small image number for training.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Kuwayama, Soichiro& Ayatsuka, Yuji& Yanagisono, Daisuke& Uta, Takaki& Usui, Hideaki& Kato, Aki…[et al.]. 2019. Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images. Journal of Ophthalmology،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1185978
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Kuwayama, Soichiro…[et al.]. Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images. Journal of Ophthalmology No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1185978
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Kuwayama, Soichiro& Ayatsuka, Yuji& Yanagisono, Daisuke& Uta, Takaki& Usui, Hideaki& Kato, Aki…[et al.]. Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images. Journal of Ophthalmology. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1185978
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1185978
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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