Yoshitsugu, K.; Shimizu, E.; Nishimura, H.; Khemlani, R.; Nakayama, S.; Takemura, T. Development of the AI Pipeline for Corneal Opacity Detection. Bioengineering2024, 11, 273.
Yoshitsugu, K.; Shimizu, E.; Nishimura, H.; Khemlani, R.; Nakayama, S.; Takemura, T. Development of the AI Pipeline for Corneal Opacity Detection. Bioengineering 2024, 11, 273.
Yoshitsugu, K.; Shimizu, E.; Nishimura, H.; Khemlani, R.; Nakayama, S.; Takemura, T. Development of the AI Pipeline for Corneal Opacity Detection. Bioengineering2024, 11, 273.
Yoshitsugu, K.; Shimizu, E.; Nishimura, H.; Khemlani, R.; Nakayama, S.; Takemura, T. Development of the AI Pipeline for Corneal Opacity Detection. Bioengineering 2024, 11, 273.
Abstract
Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of both practitioners and equipment. This study employed a portable slit-lamp microscope with video capabilities and cloud storage for a more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targeted corneal opacity, a global cause of blindness. To boost the online diagnosis efficiency, an AI pipeline was developed using anterior segment videos to detect corneal opacity. First, we extracted image frames from videos and learned them using a Convolutional Neural Network(CNN) model. Second, we manually annotated to extract only the corneal margins, adjusted the contrast with CLAHE, and learned using the CNN model. Finally, we performed semantic segmentation of the cornea using the annotated data. The results showed an accuracy of 0.8 for image frames, and 0.96 for corneal margins. Dice and IoU were 0.94 for semantic segmentation of corneal margins. While 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. Integrating manual annotation into the AI pipeline through semantic segmentation achieved a high accuracy in detecting corneal opacity.
Keywords
corneal opacity; corneal opacity detection; AI pipeline
Subject
Medicine and Pharmacology, Ophthalmology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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