Management of an inadvertent late filtering bleb and hypotony in a scleral fixated intraocular lens implantation.

PURPOSE: To report a case of inadvertent cystic filtering bleb in a patient with scleral fixated intraocular lens (SFIOL) implantation 7 years after initial procedure. CASE PRESENTATION: A 14-year-old boy underwent lensectomy, vitrectomy and a sutured rigid SFIOL for microspherophakia in the right eye. Seven years later, the patient presented with hypotony related ocular changes. Examination revealed an inadvertent cystic filtering bleb at the scleral section. Anterior segment optical coherence tomography (ASOCT) revealed gap in inner lips of the scleral wound, bridged by a thin strip of scleral tissue. A conservative management with bandage contact lens (BCL) failed after an initial improvement. Injection of trypan blue dye in the anterior chamber confirmed the presence and site of wound filtration. Surgical closure of the fistula was done and patient showed both structural and visual improvement. CONCLUSION: Meticulous wound construction and water tight closure at the end of the surgery can avoid such complications. ASOCT can be helpful in diagnosing the area of fistula. A conservative management with BCL can help by tamponading the filtering bleb. However, in unresponsive cases, surgical closure of the fistula is recommended to improve the hypotony-related changes.

OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution.

There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the Red, Green and Blue (RGB) value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images.

Relationship Between Posterior Vitreous Detachment and Macular Dimensions in Myopic Eyes.

Background: Posterior vitreous detachment (PVD) can result in serious pathologic events at the vitreoretinal interface. This study aims to assess the relationship between PVD, macular thickness (MT), and the foveal avascular zone (FAZ) in myopic eyes. Methods: This retrospective study evaluated 63 myopic subjects' data who were examined between January 1 and June 30, 2019. The myopes were grouped on a severity scale, namely mild, moderate, high, and very high using their spherical equivalent (SE). The PVD classification was based on OCT images. The status of the PVD and MT were evaluated with the Macular Cube 200×200 images, the FAZ with an OCTA Angioplex, and the dimensions were calculated using a customized algorithm. Results: The study population (114 eyes) had a median (range) age was 26.00 (22.00-28.00) years and the females constituted 62.3% of the dataset. In this population, 10 eyes had no PVD in any quadrant, and 73 eyes had incomplete PVD in all four quadrants. The inferior quadrant had the maximum rate of PVD occurrences and the nasal quadrant had the least number of occurrences. High myopic eyes exhibited significantly increased low foveal volume (p = <0.01). The inferior part of the para- and perimacular area showed a significant thinning in very high myopic eyes (p = <0.050). The very high myopic eyes showed a significant alteration of FAZ's circularity index (p = 0.002). Conclusion: In high and very high myopic eyes, an increasing trend of partial PVD is seen, most commonly in the inferior quadrant. A significant alteration in foveal volume and circularity index of the FAZ is seen in high and very high myopic eyes.

ODTiD: Optic Nerve Head SD-OCT Image Dataset.

INTRODUCTION: Optic disc tilt (ODT) or tilted optic disc is a common finding in the general population. It is due to anomalous development caused by the malclosure of the embryonic optic fissure. ODT is commonly associated with high myopia as well as other conditions. In recent days, the common method to image the optic disc (OD) is by optical coherence tomography (OCT). To the best of our knowledge, there are no datasets of ODT available in the public domain. This dataset aims to make open access raw ODT OCT images to test out new image processing segmentation algorithms. METHODS: This dataset of ODT images contains both horizontal and vertical cross-sectional images obtained using spectral-domain optical coherence tomography (SD-OCT, Cirrus 5000, Carl Zeiss Meditec Inc., Dublin, CA). The optic disc cube 200×200 program was used and all the images are aligned with the center of the optic nerve head. This dataset includes images from both clinically normal (20 eyes) and myopic subjects (101 eyes). RESULTS: The dataset consists of clear (121) and manually marked (121) images resulting in a total of 242 images. The age distribution for all subjects combined is 27.24 ± 9.28 (range, 11.0-69.0) years. For normal subjects mean ± SD age distribution is 32.40 ± 17.23 years. Similarly, the myopia age distribution is 26.22 ± 6.37 years. Ground truth images, ie, manually segmented by a clinical expert are provided along with other meta-data includes age, gender, laterality, refractive error classification, spherical equivalent (SE), best-corrected visual acuity (BCVA), intraocular pressure (IOP), and axial length (AXL). CONCLUSION: This open, public database is online at the ICPSR website of the University of Michigan. The dataset can be used to test and validate newly developed automated segmentation algorithms.

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016-2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.

Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis.

BACKGROUND: The lack of explanations for the decisions made by deep learning algorithms has hampered their acceptance by the clinical community despite highly accurate results on multiple problems. Attribution methods explaining deep learning models have been tested on medical imaging problems. The performance of various attribution methods has been compared for models trained on standard machine learning datasets but not on medical images. In this study, we performed a comparative analysis to determine the method with the best explanations for retinal OCT diagnosis. METHODS: A well-known deep learning model, Inception-v3 was trained to diagnose 3 retinal diseases - choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen. The explanations from 13 different attribution methods were rated by a panel of 14 clinicians for clinical significance. Feedback was obtained from the clinicians regarding the current and future scope of such methods. RESULTS: An attribution method based on Taylor series expansion, called Deep Taylor, was rated the highest by clinicians with a median rating of 3.85/5. It was followed by Guided backpropagation (GBP), and SHapley Additive exPlanations (SHAP). CONCLUSION: Explanations from the top methods were able to highlight the structures for each disease - fluid accumulation for CNV, the boundaries of edema for DME, and bumpy areas of retinal pigment epithelium (RPE) for drusen. The most suitable method for a specific medical diagnosis task may be different from the one considered best for conventional tasks. Overall, there was a high degree of acceptance from the clinicians surveyed in the study.

FAZSeg: A New Software for Quantification of the Foveal Avascular Zone.

INTRODUCTION: Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ's deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation). METHODS: Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420×420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth. RESULTS: The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0-66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98. CONCLUSION: A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device's inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online.

Evaluation of Hospital Referral Notes for Pulmonary Function Tests at Tertiary Care Teaching Hospital in Oman.

OBJECTIVES: There is substantial increase in referrals for investigations at Sultan Qaboos University Hospital (SQUH). Evaluating the quality of hospital referral notes (RNs) is necessary from diagnostic, patient, and economic perspectives. RNs for pulmonary function tests (PFTs) can be considered as representative of the array of tests performed at SQUH. METHODS: This cross-sectional study evaluated the quality of RNs based on 'completeness' for all consecutive patients referred for PFTs at SQUH over six months. Evaluation included documentation of demographic features, RNs date, priority for investigation, referral reason, pulmonary and relevant medical and smoking history for spirometry RNs, and addition of hemoglobin level for full lung functions (Full-LuFs) tests. The proportion of complete RNs was determined and analyzed separately to study association of spirometry and full-LuFs tests with source and reasons for referral. RESULTS: Only 644 of 683 RNs were available for analysis. Smoking history was stated in only 3.3% of all RNs; hence, it was excluded from the analysis. RNs were complete only in 12.4% of cases for spirometry and 14.4% for full-LuFs tests. RNs for patients who primarily had pulmonary disease were proportionately incomplete than those for patients with other reasons (p < 0.050). There was no significant association between incompleteness of RNs and referral hospital (SQUH vs. non-SQUH) and SQUH inpatient or outpatient status implying that the overall proportion of completeness of RNs from all concerned referral locations was similar and low in both types of RNs. CONCLUSIONS: There is a need to improve the quality of RNs at SQUH. Referring doctors must be made aware of the importance of completeness of RNs to improve test reporting, patient management and satisfaction, and reduce financial burden on hospital exchequer.

The pattern of sensitisation to inhalant allergens in omani patients with asthma, allergic rhinitis and rhinoconjunctivitis.

OBJECTIVE: Identification of relevant allergens that are prevalent in each environment which may have diagnostic and therapeutic implications in allergic diseases. This study aimed to identify the pattern of sensitisation to inhalant allergens in Omani patients with asthma, allergic rhinitis and rhinoconjunctivitis. METHODS: The study was carried out during three consecutive years (2004-2006) at the allergy skin test laboratory of Sultan Qaboos University Hospital, Oman. Records of patients who had undergone an allergy skin prick test with a referring diagnosis of asthma, allergic rhinitis or rhinoconjunctivitis were reviewed. Two panels were used during the 3 years period. The frequencies of positive skin tests were analysed. RESULTS: 689 patients were tested, 384 for the first panel and 305 for the second panel. In the first panel, the commonest positive allergens were: house dust mites (37.8%), hay dust (35.4%), feathers (33.3%), sheep wool (26.6%), mixed threshing dust (25.8%), cat fur (24.2%), cockroach (22.7%), straw dust (22.7%), horse hair (17.4%), maize (16.1%), grasses (11.5%), cotton flock (10.7%), trees (10.4%), cow hair (7.8%), Alternaria alternata (3.6%), Aspergillus Niger (3.4%), and Aspergillus fumigatus (1.3%). In the second panel, the commonest positive allergens were also house dust mites: Dermatophagoides pteronyssinus (50.8%), Dermatophagoides farinae (47.9%); Mesquite (Prosopis glandulosa) (35.7%), Russian thistle (Salsola kali) (34.4%), cockroach (32.1%), Bermuda grass (Cynodon dactylon) (19.7%), grass mix-five standard (18.0%), wheat cultivate (14.1%), cats (13.8%), Penicillium notatum (4.3%), Alternaria tenius (3.9%), Aspergillus Niger (3.3%), feather mix (3.0%), dog (2.6%), horse hair and dander (2.6%), and Aspergillus fumigatus (1.6%). CONCLUSION: The pattern of sensitisation to environmental allergens in Oman seems to be similar to other reports from the Arabian Peninsula. Methods to identify and characterise environment specific allergens like a pollen survey may help in the management of patients with allergic asthma, allergic rhinitis and rhinoconjunctivitis.