Infectious keratitis (IK) is a common ophthalmic emergency that requires prompt and accurate treatment. This study aimed to propose a deep learning (DL) system based on slit lamp images to automatically screen and diagnose infectious keratitis. This study established a dataset of 2757 slit lamp images from 744 patients, including normal cornea, viral keratitis (VK), fungal keratitis (FK), and bacterial keratitis (BK). Six different DL algorithms were developed and evaluated for the classification of infectious keratitis. Among all the models, the EffecientNetV2-M showed the best classification ability, with an accuracy of 0.735, a recall of 0.680, and a specificity of 0.904, which was also superior to two ophthalmologists. The area under the receiver operating characteristics curve (AUC) of the EffecientNetV2-M was 0.85; correspondingly, 1.00 for normal cornea, 0.87 for VK, 0.87 for FK, and 0.64 for BK. The findings suggested that the proposed DL system could perform well in the classification of normal corneas and different types of infectious keratitis, based on slit lamp images. This study proves the potential of the DL model to help ophthalmologists to identify infectious keratitis and improve the accuracy and efficiency of diagnosis.
Purpose: To develop a deep learning method to automatically monitor the implantable collamer lens (ICL) position and quantify subtle alterations in the anterior chamber using anterior segment optical coherence tomography (AS-OCT) images for high myopia patients with ICL implantation.Methods: In this study, 798 AS-OCT images of 203 patients undergoing ICL implantation at our eye center from April 2017 to June 2021 were involved. A deep learning system was developed to first isolate the corneoscleral, ICL, and lens, and then quantify clinical important parameters in AS-OCT images (central corneal thickness, anterior chamber depth, and lens vault).Results: The deep learning system was able to accurately isolate the corneoscleral, ICL, and lens with the Dice coefficient ranging from 0.911 to 0.960, and all the F1 scores >0.900. The relative error between automated measurements and the ground truth for 95% (188 images out of 198) of LVs was within 10%. Intraclass correlation coefficients (ICCs) of the machine-ground truth measurements ranged from 0.928 to 0.995. The deep learning method also showed better repeatability than human graders.Conclusion: The deep learning method provides reliable detection and quantification of AS-OCT scans for postoperative ICL implantation, which can simplify and optimize the management of clinical outcomes of ICL implantations. Also, this is a step towards an objective measurement of the postoperative vault, making the data more comparable and repeatable to each other.
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