Purpose. The present study highlighted the value of anterior segment optical coherence tomography (AS-OCT) for different types of corneal foreign bodies in humans. Methods. This study was a prospective observational study. The patients included were divided into two groups. If the patients were directly diagnosed based on eye injury history and slit-lamp examination, then they were assigned to Group A. Otherwise, the patients were assigned to Group B. We compared and described the characteristics of the corneal foreign body in both groups using AS-OCT. Results. From October 2017 to January 2020, 36 eyes of 36 patients (9 females and 27 males) with a mean age of 37.8 ± 11.7 years were included in the study. Patients in Group A were the majority and accounted for 72.2% (26/36). High signals on AS-OCT images were the main constituent and accounted for 92.3% (24/26) in Group A and 70.0% (7/10) in Group B. Most of the patients in Group A, 96.2% (25/26), had clear boundaries. A blurred boundary was observed in 70.0% (7/10) of the patients in Group B. The foreign bodies on AS-OCT images had key characteristics of a high signal followed by a central zone shadowing effect and a low signal followed by a marginal zone shadowing effect. Further, all of the lesions could be directly located in Group B, and 92.3% (24/26) of the patients in Group A did not have directly located lesions. Six representative cases are described in detail. Conclusions. AS-OCT is a valuable tool in the diagnosis and management of corneal foreign bodies, especially for unusual corneal foreign body.
Background: Deep learning has had a large effect on medical fields, including ophthalmology. The goal of this study was to quantitatively analyze the functional filtering bleb size with Mask R-CNN. Methods: This observational study employed eighty-three images of post-trabeculectomy functional filtering blebs. The images were divided into training and test groups and scored according to the Indiana Bleb Appearance Grading Scale (IBAGS) system. Then, 70 images from the training group were used to train an automatic detection system based on Mask R-CNN and perform a quantitative analysis of the function bleb size. Thirteen images from the test group were used to evaluate the model. During the training process, left and right image-flipping algorithms were used for data augmentation. Finally, the correlation between the functional filtering bleb area and the intraocular pressure (IOP) was analyzed. Results: The 83 functional filtering blebs have similar morphological features. According to IBAGS, the functional filtering blebs have a high incidence of E1/E2, H1/H2, and V0/V1. Our Mask R-CNN-based model using the selected parameters achieves good results on the training group after a 200-epoch training process. All the Intersection over Union (IoU) scores exceeded 93% on the test group. The Spearman correlation coefficient between the area of functional filtering blebs and the IOP value was −0.757 (P<0.05).Conclusions: Deep learning is a powerful tool for quantitatively analyzing the functional filtering bleb size. This technique is suitable for use in monitoring post-trabeculectomy filtering blebs in the future.
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