Development of the AI Pipeline for Corneal Opacity Detection
Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resour...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/3/273 |
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| author | Kenji Yoshitsugu Eisuke Shimizu Hiroki Nishimura Rohan Khemlani Shintaro Nakayama Tadamasa Takemura |
| author_facet | Kenji Yoshitsugu Eisuke Shimizu Hiroki Nishimura Rohan Khemlani Shintaro Nakayama Tadamasa Takemura |
| author_sort | Kenji Yoshitsugu |
| collection | DOAJ |
| description | Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targets corneal opacity, which is a global cause of blindness. This study has two purposes. The first is to detect corneal opacity from videos in which the anterior segment of the eye is captured. The other is to develop an AI pipeline to detect corneal opacities. First, we extracted image frames from videos and processed them using a convolutional neural network (CNN) model. Second, we manually annotated the images to extract only the corneal margins, adjusted the contrast with CLAHE, and processed them using the CNN model. Finally, we performed semantic segmentation of the cornea using annotated data. The results showed an accuracy of 0.8 for image frames and 0.96 for corneal margins. Dice and IoU achieved a score of 0.94 for semantic segmentation of the corneal margins. Although corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. The incorporation of manual annotation into the AI pipeline, through semantic segmentation, facilitated high accuracy in detecting corneal opacity. |
| first_indexed | 2024-04-24T18:32:43Z |
| format | Article |
| id | doaj.art-cafd32354e314a44aaceade2deacf995 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-04-24T18:32:43Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-cafd32354e314a44aaceade2deacf9952024-03-27T13:21:56ZengMDPI AGBioengineering2306-53542024-03-0111327310.3390/bioengineering11030273Development of the AI Pipeline for Corneal Opacity DetectionKenji Yoshitsugu0Eisuke Shimizu1Hiroki Nishimura2Rohan Khemlani3Shintaro Nakayama4Tadamasa Takemura5Graduate School of Information Science, University of Hyogo, Kobe Information Science Campus, Kobe 6500047, JapanOUI Inc., Tokyo 1070062, JapanOUI Inc., Tokyo 1070062, JapanOUI Inc., Tokyo 1070062, JapanOUI Inc., Tokyo 1070062, JapanGraduate School of Information Science, University of Hyogo, Kobe Information Science Campus, Kobe 6500047, JapanOphthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targets corneal opacity, which is a global cause of blindness. This study has two purposes. The first is to detect corneal opacity from videos in which the anterior segment of the eye is captured. The other is to develop an AI pipeline to detect corneal opacities. First, we extracted image frames from videos and processed them using a convolutional neural network (CNN) model. Second, we manually annotated the images to extract only the corneal margins, adjusted the contrast with CLAHE, and processed them using the CNN model. Finally, we performed semantic segmentation of the cornea using annotated data. The results showed an accuracy of 0.8 for image frames and 0.96 for corneal margins. Dice and IoU achieved a score of 0.94 for semantic segmentation of the corneal margins. Although corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. The incorporation of manual annotation into the AI pipeline, through semantic segmentation, facilitated high accuracy in detecting corneal opacity.https://www.mdpi.com/2306-5354/11/3/273deep learningsemantic segmentationcorneal opacity detectionAI pipeline |
| spellingShingle | Kenji Yoshitsugu Eisuke Shimizu Hiroki Nishimura Rohan Khemlani Shintaro Nakayama Tadamasa Takemura Development of the AI Pipeline for Corneal Opacity Detection Bioengineering deep learning semantic segmentation corneal opacity detection AI pipeline |
| title | Development of the AI Pipeline for Corneal Opacity Detection |
| title_full | Development of the AI Pipeline for Corneal Opacity Detection |
| title_fullStr | Development of the AI Pipeline for Corneal Opacity Detection |
| title_full_unstemmed | Development of the AI Pipeline for Corneal Opacity Detection |
| title_short | Development of the AI Pipeline for Corneal Opacity Detection |
| title_sort | development of the ai pipeline for corneal opacity detection |
| topic | deep learning semantic segmentation corneal opacity detection AI pipeline |
| url | https://www.mdpi.com/2306-5354/11/3/273 |
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