Automated tear film break-up time measurement for dry eye diagnosis using deep learning.

In the realm of ophthalmology, precise measurement of tear film break-up time (TBUT) plays a crucial role in diagnosing dry eye disease (DED). This study aims to introduce an automated approach utilizing artificial intelligence (AI) to mitigate subjectivity and enhance the reliability of TBUT measurement. We employed a dataset of 47 slit lamp videos for development, while a test dataset of 20 slit lamp videos was used for evaluating the proposed approach. The multistep approach for TBUT estimation involves the utilization of a Dual-Task Siamese Network for classifying video frames into tear film breakup or non-breakup categories. Subsequently, a postprocessing step incorporates a Gaussian filter to smooth the instant breakup/non-breakup predictions effectively. Applying a threshold to the smoothed predictions identifies the initiation of tear film breakup. Our proposed method demonstrates on the evaluation dataset a precise breakup/non-breakup classification of video frames, achieving an Area Under the Curve of 0.870. At the video level, we observed a strong Pearson correlation coefficient (r) of 0.81 between TBUT assessments conducted using our approach and the ground truth. These findings underscore the potential of AI-based approaches in quantifying TBUT, presenting a promising avenue for advancing diagnostic methodologies in ophthalmology.

Association of Combined Anti-Ro52/TRIM21 and Anti-Ro60/SSA Antibodies With Increased Sjögren Disease Severity Through Interferon Pathway Activation.

OBJECTIVE: The biologic diagnosis of primary Sjögren disease (SjD) mainly relies on anti-Ro60/SSA antibodies, whereas the significance of anti-Ro52/TRIM21 antibodies currently remains unclear. The aim of this study was to characterize the clinical, serological, biologic, transcriptomic, and interferon profiles of patients with SjD according to their anti-Ro52/TRIM21 antibody status. METHODS: Patients with SjD from the European PRECISESADS (n = 376) and the Brittany Diagnostic Suspicion of primitive Sjögren's Syndrome (DIApSS); (n = 146) cohorts were divided into four groups: double negative (Ro52-/Ro60-), isolated anti-Ro52/TRIM21 positive (Ro52+), isolated anti-Ro60/SSA positive (Ro60+), and double-positive (Ro52+/Ro60+) patients. Clinical information; EULAR Sjögren Syndrome Disease Activity Index, a score representing systemic activity; and biologic markers associated with disease severity were evaluated. Transcriptome data obtained from whole blood by RNA sequencing and type I and II interferon signatures were analyzed for PRECISESADS patients. RESULTS: In the DIApSS cohort, Ro52+/Ro60+ patients showed significantly more parotidomegaly (33.3% vs 0%-11%) along with higher ß2-microglobulin (P = 0.0002), total immunoglobulin (P < 0.0001), and erythrocyte sedimentation rate levels (P = 0.002) as well as rheumatoid factor (RF) positivity (66.2% vs 20.8%-25%) compared to other groups. The PRECISESADS cohort corroborated these observations, with increased arthritis (P = 0.046), inflammation (P = 0.005), hypergammaglobulinemia (P < 0.0001), positive RF (P < 0.0001), leukopenia (P = 0.004), and lymphopenia (P = 0.009) in Ro52+/Ro60+ patients. Cumulative EULAR Sjögren Syndrome Disease Activity Index results further confirmed these disparities (P = 0.002). Transcriptome analysis linked anti-Ro52/TRIM21 antibody positivity to interferon pathway activation as an underlying cause for these clinical correlations. CONCLUSION: These results suggest that the combination of anti-Ro52/TRIM21 and anti-Ro60/SSA antibodies is associated with a clinical, biologic, and transcriptional profile linked to greater disease severity in SjD through the potentiation of the interferon pathway activation by anti-Ro52/TRIM21 antibodies.

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.].

Automation of dry eye disease quantitative assessment: A review.

Dry eye disease (DED) is a common eye condition worldwide and a primary reason for visits to the ophthalmologist. DED diagnosis is performed through a combination of tests, some of which are unfortunately invasive, non-reproducible and lack accuracy. The following review describes methods that diagnose and measure the extent of eye dryness, enabling clinicians to quantify its severity. Our aim with this paper is to review classical methods as well as those that incorporate automation. For only four ways of quantifying DED, we take a deeper look into what main elements can benefit from automation and the different ways studies have incorporated it. Like numerous medical fields, Artificial Intelligence (AI) appears to be the path towards quality DED diagnosis. This review categorises diagnostic methods into the following: classical, semi-automated and promising AI-based automated methods.

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.