To characterize the clinical features in young patients with angle closure and to determine the characteristics associated with acquired anterior segment abnormality following retinopathy of prematurity (ROP) treatment.We performed two retrospective case-control series. In the first series, we identified consecutive young angle closure patients without prior surgeries, with and without a history of ROP treatment; in the second series we identified consecutive patients who underwent ROP treatment, without and without anterior segment changes.In the first series, 25 eyes of 14 consecutive angle closure patients were included: 19 eyes (11 patients, 78.6%) had a history of treated ROP, while 6 eyes (3 patients) belonged to full-term patients. The treated ROP eyes had significantly shallower anterior chambers (1.77 ± 0.17 mm vs 2.72 ± 0.18 mm, P < 0.0001) and thicker lenses (5.20 ± 0.54 mm vs 3.98 ± 0.20 mm, P = 0.0002) compared to the full-term controls. In the second series, 79 eyes of 40 patients were included, with median gestational age of 24.6 weeks. Acquired iridocorneal adhesion was noted in the eight eyes (10.1%) at a mean age of 4.7 years and was associated with prior zone 1 and plus disease (P = 0.0013), a history of initial intravitreal bevacizumab treatment (IVB, P = 0.0477) and a history of requiring additional IVB after initial treatment (P = 0.0337).Many young angle closure patients may have a history of treated ROP and may present with the triad of increased lens thickness, microcornea, and angle closure.
The authors report the long-term results of combined conjunctival autograft and overlay amniotic membrane transplantation (AMT) for treatment of pterygium as a new surgical technique. Nineteen patients including 12 male and 7 female subjects with pterygium (primary, 14 cases; recurrent, 5 cases) underwent combined conjunctival autograft and overlay AMT and were followed from 10 to 26 months. Mean age was 44.21±12.49 (range, 29.0-73.0) years. In one patient with grade T3 primary pterygium, the lesion recurred (5.2%, recurrence rate). No intra-and postoperative complication developed. This procedure seems a safe and effective surgical technique for pterygium treatment. Protection of the ocular surface during the early postoperative period reduces the friction-induced inflammation and might be helpful to prevent the recurrence.
To propose automatic segmentation algorithm (AUS) for corneal microlayers on optical coherence tomography (OCT) images.Eighty-two corneal OCT scans were obtained from 45 patients with normal and abnormal corneas. Three testing data sets totaling 75 OCT images were randomly selected. Initially, corneal epithelium and endothelium microlayers are estimated using a corneal mask and locally refined to obtain final segmentation. Flat-epithelium and flat-endothelium images are obtained and vertically projected to locate inner corneal microlayers. Inner microlayers are estimated by translating epithelium and endothelium microlayers to detected locations then refined to obtain final segmentation. Images were segmented by trained manual operators (TMOs) and by the algorithm to assess repeatability (i.e., intraoperator error), reproducibility (i.e., interoperator and segmentation errors), and running time. A random masked subjective test was conducted by corneal specialists to subjectively grade the segmentation algorithm.Compared with the TMOs, the AUS had significantly less mean intraoperator error (0.53 ± 1.80 vs. 2.32 ± 2.39 pixels; P < 0.0001), it had significantly different mean segmentation error (3.44 ± 3.46 vs. 2.93 ± 3.02 pixels; P < 0.0001), and it had significantly less running time per image (0.19 ± 0.07 vs. 193.95 ± 194.53 seconds; P < 0.0001). The AUS had insignificant subjective grading for microlayer-segmentation grading (4.94 ± 0.32 vs. 4.96 ± 0.24; P = 0.5081), but it had significant subjective grading for regional-segmentation grading (4.96 ± 0.26 vs. 4.79 ± 0.60; P = 0.025).The AUS can reproduce the manual segmentation of corneal microlayers with comparable accuracy in almost real-time and with significantly better repeatability.The AUS can be useful in clinical settings and can aid the diagnosis of corneal diseases by measuring thickness of segmented corneal microlayers.
Convolutional neural network (CNN) can be applied in glaucoma detection for achieving good performance. However, its performance depends on the availability of a large number of the labelled samples for its training phase. To solve this problem, this paper present a semi-supervised transfer learning CNN model for automatic glaucoma detection based on both labeled and unlabeled data. First, a pre-trained CNN from non-medical data is fine-tuned and trained in a supervised fashion using the labeled data. The self-learning approach is then used to predict the labels for the unlabeled data and utilize it for training. The experimental results on the RIM-ONE database demonstrate the effectiveness of the proposed algorithm despite the lack of initial labeled samples.
To evaluate a deep learning-based method to autonomously detect dry eye disease (DED) in anterior segment optical coherence tomography (AS-OCT) images compared to common clinical dry eye tests.In this study, 27,180 AS-OCT images were prospectively collected from 151 eyes of 91 patients. Images were used to train and test the deep learning model. Masked cornea specialist ophthalmologist diagnoses were used as the gold standard. Clinical dry eye tests were performed on patients in the DED group to compare the results of the model. The dry eye tests performed were tear break-up time (TBUT), Schirmer's test, corneal staining, conjunctival staining, and Ocular Surface Disease Index (OSDI).Our deep learning model achieved an accuracy of 84.62%, sensitivity of 86.36%, and specificity of 82.35% in the diagnosis of DED. The positive likelihood ratio was 4.89, and the negative likelihood ratio was 0.17. The mean DED probability score was 0.81 ± 0.23 in the DED group and 0.20 ± 0.27 in the healthy group (P < 0.01). The deep learning model accuracy in the diagnosis of DED was significantly better than that of corneal staining, conjunctival staining, and Schirmer's test (P < 0.05). There was no significant difference between the deep learning diagnostic accuracy and that of the OSDI and TBUT.Based on preliminary results, reliable autonomous diagnosis of DED with our deep learning model was achieved, when compared with standard dry eye clinical tests that correlated significantly more or similarly to diagnoses made by cornea specialist ophthalmologists.
Various common corneal eye diseases, such as dry eye, Fuchs endothelial dystrophy, Keratoconus and corneal graft rejection, can be diagnosed based on the changes in the thickness of corneal microlayers. Optical Coherence Tomography (OCT) technology made it possible to obtain high resolution corneal images that show the microlayered structures of the cornea. Manual segmentation is subjective and not feasible due to the large volume of obtained images. Existing automatic methods, used for segmenting corneal layer interfaces, are not robust and they segment few corneal microlayer interfaces. Moreover, there is no large annotated database of corneal OCT images, which is an obstacle towards the application of powerful machine learning methods such as deep learning for the segmentation of corneal interfaces. In this paper, we propose a novel segmentation method for corneal OCT images using Graph Search and Radon Transform. To the best of our knowledge, we are the first to develop an automatic segmentation method for the six corneal microlayer interfaces. The proposed method involves a novel image denoising method and an inner interfaces localization method. The proposed method was tested on 15 corneal OCT images. The images were randomly selected and manually segmented by two operators. Experimental results show that our method has a mean segmentation error of 3.87 ± 5.21 pixels (i.e. 5.81 ± 7.82μm) across all interfaces compared to the segmentation of the manual operators. The two manual operators have mean segmentation difference of 4.07 ± 4.71 pixels (i.e. 6.11 ± 7.07μm). The mean running time to segment all the corneal microlayer interfaces is 6.66 ± 0.22 seconds.
