French national diagnostic and care protocol for Sjögren's disease.

Sjögren's disease (SD), also known as Sjögren's syndrome (SS) or Gougerot-Sjögren's syndrome in France, is a rare systemic autoimmune disease in its primary form and is characterised by tropism for the exocrine glandular epithelia, particularly the salivary and lacrimal glands. The lymphocytic infiltration of these epithelia will clinically translate into a dry syndrome which, associated with fatigue and pain, constitutes the symptom triad of the disease. In about one third of patients, SD is associated with systemic complications that can affect the joints, skin, lungs, kidneys, central or peripheral nervous system, and lymphoid organs with an increased risk of B-cell lymphoma. SD affects women more frequently than men (9/1). The peak frequency is around the age of 50. However, the disease can occur at any age, with paediatric forms occurring even though they remain rare. SD can occur alone or in association with other systemic autoimmune diseases. In its isolated or primary form, the prevalence of SD is estimated to be between 1 per 1000 and 1 per 10,000 inhabitants. The most recent classification criteria were developed in 2016 by EULAR and ACR. The course and prognosis of the disease are highly variable and depend on the presence of systemic involvement and the severity of the dryness of the eyes and mouth. The current approach is therefore to identify at an early stage those patients most at risk of systemic complications or lymphoma, who require close follow-up. On the other hand, regular monitoring of the ophthalmological damage and of the dental status should be ensured to reduce the consequences.

Comparison of Bilateral Reading Performance Among Two Presbyopia-Correcting Intraocular Lenses.

PURPOSE: To evaluate and compare the performance of a trifocal diffractive intraocular lens (IOL) and a lens combining a bifocal diffractive profile and extended depth of focus (EDOF) profile. METHODS: This non-randomized, prospective comparative study included 42 patients (84 eyes) undergoing lens surgery with implantation of either the FineVision HP trifocal IOL (PhysIOL) or TECNIS Synergy bifocal EDOF IOL (Johnson and Johnson Surgical Vision). There were 21 patients (42 eyes) in each group. The primary outcome was reading speed at high contrast and luminance. Secondary outcomes were reading speed at lower contrasts and luminances, visual acuity at all distances (distance, intermediate, and near) with and without correction, and quality of vision. RESULTS: The reading speed at high contrast (100%) and high luminance (100%) was better in the Synergy group (P = .01). This difference between the two IOLs seemed to be preserved at lower contrasts and luminances. There was no statistically significant difference between visual acuities except for monocular uncorrected intermediate visual acuity (P = .046) in favor of the FineVision HP IOL. The mean spherical equivalents in the FineVision HP and Synergy groups were 0.14 ± 0.64 and 0.10 ± 0.33 diopters without significant difference between these means (P = .78). The defocus curve was more dome-shaped for the Synergy IOL. The evaluation of visual symptoms was comparable in both groups. The glare halo (Halometry test; Aston University) was smaller in the FineVision HP group (P = .03). CONCLUSIONS: The Synergy IOL appears to provide better reading speed and is less sensitive to refractive error. Both lenses provided excellent distance, intermediate, and near vision. [J Refract Surg. 2022;38(7):428-434.].

Automatic Screening for Ocular Anomalies Using Fundus Photographs.

SIGNIFICANCE: Screening for ocular anomalies using fundus photography is key to prevent vision impairment and blindness. With the growing and aging population, automated algorithms that can triage fundus photographs and provide instant referral decisions are relevant to scale-up screening and face the shortage of ophthalmic expertise. PURPOSE: This study aimed to develop a deep learning algorithm that detects any ocular anomaly in fundus photographs and to evaluate this algorithm for "normal versus anomalous" eye examination classification in the diabetic and general populations. METHODS: The deep learning algorithm was developed and evaluated in two populations: the diabetic and general populations. Our patient cohorts consist of 37,129 diabetic patients from the OPHDIAT diabetic retinopathy screening network in Paris, France, and 7356 general patients from the OphtaMaine private screening network, in Le Mans, France. Each data set was divided into a development subset and a test subset of more than 4000 examinations each. For ophthalmologist/algorithm comparison, a subset of 2014 examinations from the OphtaMaine test subset was labeled by a second ophthalmologist. First, the algorithm was trained on the OPHDIAT development subset. Then, it was fine-tuned on the OphtaMaine development subset. RESULTS: On the OPHDIAT test subset, the area under the receiver operating characteristic curve for normal versus anomalous classification was 0.9592. On the OphtaMaine test subset, the area under the receiver operating characteristic curve was 0.8347 before fine-tuning and 0.9108 after fine-tuning. On the ophthalmologist/algorithm comparison subset, the second ophthalmologist achieved a specificity of 0.8648 and a sensitivity of 0.6682. For the same specificity, the fine-tuned algorithm achieved a sensitivity of 0.8248. CONCLUSIONS: The proposed algorithm compares favorably with human performance for normal versus anomalous eye examination classification using fundus photography. Artificial intelligence, which previously targeted a few retinal pathologies, can be used to screen for ocular anomalies comprehensively.