Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images
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 O...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Ophthalmology |
| Online Access: | http://dx.doi.org/10.1155/2019/6319581 |
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| author | Soichiro Kuwayama Yuji Ayatsuka Daisuke Yanagisono Takaki Uta Hideaki Usui Aki Kato Noriaki Takase Yuichiro Ogura Tsutomu Yasukawa |
| author_facet | Soichiro Kuwayama Yuji Ayatsuka Daisuke Yanagisono Takaki Uta Hideaki Usui Aki Kato Noriaki Takase Yuichiro Ogura Tsutomu Yasukawa |
| author_sort | Soichiro Kuwayama |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2d3f7e90cde84f13b5da02d66d115e4c |
| institution | Kabale University |
| issn | 2090-004X 2090-0058 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Ophthalmology |
| spelling | doaj-art-2d3f7e90cde84f13b5da02d66d115e4c2025-02-03T01:32:45ZengWileyJournal of Ophthalmology2090-004X2090-00582019-01-01201910.1155/2019/63195816319581Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography ImagesSoichiro Kuwayama0Yuji Ayatsuka1Daisuke Yanagisono2Takaki Uta3Hideaki Usui4Aki Kato5Noriaki Takase6Yuichiro Ogura7Tsutomu Yasukawa8Department of Ophthalmology & Visual Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, JapanTechnology Laboratory, Cresco Ltd., Tokyo, JapanTechnology Laboratory, Cresco Ltd., Tokyo, JapanTechnology Laboratory, Cresco Ltd., Tokyo, JapanDepartment of Ophthalmology & Visual Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, JapanDepartment of Ophthalmology & Visual Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, JapanDepartment of Ophthalmology & Visual Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, JapanDepartment of Ophthalmology & Visual Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, JapanDepartment of Ophthalmology & Visual Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, JapanPurpose. 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.http://dx.doi.org/10.1155/2019/6319581 |
| spellingShingle | Soichiro Kuwayama Yuji Ayatsuka Daisuke Yanagisono Takaki Uta Hideaki Usui Aki Kato Noriaki Takase Yuichiro Ogura Tsutomu Yasukawa Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images Journal of Ophthalmology |
| title | Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images |
| title_full | Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images |
| title_fullStr | Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images |
| title_full_unstemmed | Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images |
| title_short | Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images |
| title_sort | automated detection of macular diseases by optical coherence tomography and artificial intelligence machine learning of optical coherence tomography images |
| url | http://dx.doi.org/10.1155/2019/6319581 |
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