Analysis of Scheimpflug Tomography Parameters for Detecting Subclinical Keratoconus in the Fellow Eyes of Patients with Unilateral Keratoconus in the Eastern Province of Saudi Arabia.

Purpose: We compared the characteristics of subtle morphological changes in subclinical keratoconus (KC) and normal corneas using Scheimpflug tomography (Pentacam®) and assessed the efficacy of these parameters for distinguishing KC or subclinical KC from normal eyes. Patients and Methods: In this multicenter comparative study at Dhahran Eye Specialist Hospital and Al Kahhal Medical Complex in the Eastern Province of Saudi Arabia, we analyzed the Scheimpflug tomography charts of patients with topographically normal eyes and those with unilateral KC. Patients were divided into the normal (NL: patients considered for refractive surgery and with normal topographic/tomographic features, 129 eyes), KC (30 patients with manifest KC in one eye based on biomicroscopy and topographical findings), and forme fruste KC (FFKC: fellow eyes of patients in the KC group that met the NL group criteria) groups. Corneal morphological parameters were analyzed using the area under the receiver operating characteristic (ROC) curves (AUCs). Results: For distinguishing NL and KC groups, all measured corneal morphological parameters, except for flat keratometry, maximum Ambrósio relational thickness index, and minimum sagittal curvature, had AUCs >0.75. The surface variance index yielded the largest AUC (0.999). For distinguishing NL and FFKC groups, all corneal morphological parameters had AUCs <0.8. Total higher-order aberrations (RMS HOA) yielded the highest AUC, followed by Belin/Ambrosio Enhanced Ectasia total deviation (BAD-D), back elevation at the thinnest location, average pachymetric progression index (PPIave), and deviation of Ambrosio relational thickness (Da) (AUC 0.74-0.78). Conclusion: The diagnostic performance of all tested topographic and tomographic parameters measured using Scheimpflug tomography for discriminating subclinical KC was fair at best, with the top parameters being RMS HOA, BAD-D, back elevation at the thinnest location, PPIave, and Da. Distinguishing between subclinical KC and healthy eyes remains challenging. Multimodal imaging techniques may be required for optimal early detection of subtle morphological changes.

Isolated Kaposi's Sarcoma of the Bulbar Conjunctiva as an Initial Manifestation of AIDS: A Case Report.

Kaposi's sarcoma (KS) is a malignant vascular endothelium-cell-derived tumor caused by human herpesvirus 8. It is one of the most common tumors among human immunodeficiency virus (HIV)-infected patients; however, isolated KS is rarely reported as the initial presentation. This study describes a rare case in which isolated KS of the bulbar conjunctiva was the first presenting symptom leading to the diagnosis of HIV/acquired immunodeficiency syndrome (AIDS) in a 39-year-old man. The patient, who had no prior medical history, presented to the ophthalmology clinic with an isolated large, dark-reddish mass in the left bulbar conjunctiva and subconjunctival hemorrhage. The mass was first identified 6 months prior and had continued to grow since then. KS was confirmed based on the analysis of the incisional biopsy sample, subsequently prompting an HIV test, which was positive. This report highlights the recognition of KS as a relevant ocular complication and potential initial manifestation of AIDS. Additionally, KS should be considered in the differential diagnosis of any vascular lesion, even when present at uncommon sites.

Capillary Hemangioma in Joubert Syndrome: A Case Report.

A baby girl who underwent cesarean section delivery and had a complicated postnatal course requiring neonatal intensive care unit (NICU) is followed in the pediatrics clinic for several months. At five months old, the baby girl was referred to an ophthalmology clinic with brain stem and cerebellum malformation consistent with the molar tooth sign (MTS) on magnetic resonance imaging (MRI) of the brain, hypotonia, and developmental delay. She has the classic features of Joubert Syndrome (JS). Other findings not typically associated with the clinical picture of the syndrome were observed in this patient, specifically skin capillary hemangioma of the forehead. Cutaneous capillary hemangioma was an incidental finding in this JS patient and responded favorably to medical treatment with propranolol where a significant reduction in the size of the mass was observed. This incidental finding can be seen as a potential addition to the spectrum of associated findings in JS.

Learning to Discover Explainable Clinical Features With Minimum Supervision.

Purpose: To compare supervised transfer learning to semisupervised learning for their ability to learn in-depth knowledge with limited data in the optical coherence tomography (OCT) domain. Methods: Transfer learning with EfficientNet-B4 and semisupervised learning with SimCLR are used in this work. The largest public OCT dataset, consisting of 108,312 images and four categories (choroidal neovascularization, diabetic macular edema, drusen, and normal) is used. In addition, two smaller datasets are constructed, containing 31,200 images for the limited version and 4000 for the mini version of the dataset. To illustrate the effectiveness of the developed models, local interpretable model-agnostic explanations and class activation maps are used as explainability techniques. Results: The proposed transfer learning approach using the EfficientNet-B4 model trained on the limited dataset achieves an accuracy of 0.976 (95% confidence interval [CI], 0.963, 0.983), sensitivity of 0.973 and specificity of 0.991. The semisupervised based solution with SimCLR using 10% labeled data and the limited dataset performs with an accuracy of 0.946 (95% CI, 0.932, 0.960), sensitivity of 0.941, and specificity of 0.983. Conclusions: Semisupervised learning has a huge potential for datasets that contain both labeled and unlabeled inputs, generally, with a significantly smaller number of labeled samples. The semisupervised based solution provided with merely 10% labeled data achieves very similar performance to the supervised transfer learning that uses 100% labeled samples. Translational Relevance: Semisupervised learning enables building performant models while requiring less expertise effort and time by using to good advantage the abundant amount of available unlabeled data along with the labeled samples.

Automated feature-based grading and progression analysis of diabetic retinopathy.

BACKGROUND: In diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade. In this work, we demonstrate a feature based retinal image analysis system, which aims to support flexible grading and monitor progression. METHODS: The system was evaluated against images that had been graded according to two different grading systems; The International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Severity Scale and the UK's National Screening Committee guidelines. RESULTS: External evaluation on large datasets collected from three nations (Kenya, Saudi Arabia and China) was carried out. On a DR referable level, sensitivity did not vary significantly between different DR grading schemes (91.2-94.2.0%) and there were excellent specificity values above 93% in all image sets. More importantly, no cases of severe non-proliferative DR, proliferative DR or DMO were missed. CONCLUSIONS: We demonstrate the potential of an AI feature-based DR grading system that is not constrained to any specific grading scheme.

Evidence Based Prediction and Progression Monitoring on Retinal Images from Three Nations.

Purpose: The aim of this work is to demonstrate how a retinal image analysis system, DAPHNE, supports the optimization of diabetic retinopathy (DR) screening programs for grading color fundus photography. Method: Retinal image sets, graded by trained and certified human graders, were acquired from Saudi Arabia, China, and Kenya. Each image was subsequently analyzed by the DAPHNE automated software. The sensitivity, specificity, and positive and negative predictive values for the detection of referable DR or diabetic macular edema were evaluated, taking human grading or clinical assessment outcomes to be the gold standard. The automated software's ability to identify co-pathology and to correctly label DR lesions was also assessed. Results: In all three datasets the agreement between the automated software and human grading was between 0.84 to 0.88. Sensitivity did not vary significantly between populations (94.28%-97.1%) with specificity ranging between 90.33% to 92.12%. There were excellent negative predictive values above 93% in all image sets. The software was able to monitor DR progression between baseline and follow-up images with the changes visualized. No cases of proliferative DR or DME were missed in the referable recommendations. Conclusions: The DAPHNE automated software demonstrated its ability not only to grade images but also to reliably monitor and visualize progression. Therefore it has the potential to assist timely image analysis in patients with diabetes in varied populations and also help to discover subtle signs of sight-threatening disease onset. Translational Relevance: This article takes research on machine vision and evaluates its readiness for clinical use.