Search

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening.

Objectives: To investigate the impact of qualitatively graded and deep learning quantified imaging biomarkers on growth of geographic atrophy (GA) secondary to age-related macular degeneration. Methods: This prospective study included 1062 visits of 181 eyes of 100 patients with GA. Spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) images were acquired at each visit. Hyperreflective foci (HRF) were quantitatively assessed in SD-OCT volumes using a validated deep learning algorithm. FAF images were graded for FAF patterns, subretinal drusenoid deposits (SDD), GA lesion configuration and atrophy enlargement. Linear mixed models were calculated to investigate associations between all parameters and GA progression. Results: FAF patterns were significantly associated with GA progression (p< 0.001). SDD was associated with faster GA growth (p= 0.005). Eyes with higher HRF concentrations showed a trend towards faster GA progression (p= 0.072) and revealed a significant impact on GA enlargement in interaction with FAF patterns (p= 0.01). The fellow eye status had no significant effect on lesion enlargement (p> 0.05). The diffuse-trickling FAF pattern exhibited significantly higher HRF concentrations than any other pattern (p< 0.001). Conclusion: Among a wide range of investigated biomarkers, SDD and FAF patterns, particularly in interaction with HRF, significantly impact GA progression. Fully automated quantification of retinal imaging biomarkers such as HRF is both reliable and merited as HRF are indicators of retinal pigment epithelium dysmorphia, a central pathogenetic mechanism in GA. Identifying disease markers using the combination of FAF and SD-OCT is of high prognostic value and facilitates individualized patient management in a clinical setting.

This study investigates visualization of optimized layer thickness with a ternary diagram by considering Volume, Weight, and Cost priorities to determine the composite structure of alternative ceramics to use in body armor application by using the Digital Logic Method (DLM). Three criterion priorities (volume, weight, cost) have been investigated to help designers decide on optimum ceramic material for their applications. Alumina (Al2O3), silicon carbide (SiC), silicon nitride (Si3N4), and boron carbide (B4C) were analyzed and ranked to decide for material selection based on priorities. The analysis results showed that silicon nitride (Si3N4) had the maximum performance index (PI) point (80.0) based on the volume priority. On the other hand, while boron carbide (B4C) had the maximum PI point (76.4) in terms of the weight priority, alumina (Al2O3) was determined to be the best material according to the cost priority. PI point of alumina (Al2O3) was calculated as 100. A ternary diagram was developed for decision-makers to visualize material selection performances. The optimization of the ceramic composite layer thickness of the alternative ceramic materials is visualized by considering three criteria.

Oxidation creates an important problem with the increasing temperature and additionally if one of the components contains an amount of carbon, then there will be a COxemission risk which has a detrimental effect for the atmosphere. One of the methods to prevent the oxidation of silicon carbide (SiC) is to coat it with Al2O3which has high thermal stability. In this study, the properties of Al2O3coated SiC composite powders were examined to prevent the oxidation of SiC particles by developing a thermal barrier coating and it is aimed to contribute for reducing the gases which make a greenhouse effect and cause global warming. Firstly, boehmite sol was prepared via the sol gel route and then 5 wt% β-SiC (< 100 nm) nanosized particles were added to the boehmite sol and gelation was completed. Alumina coated SiC powders were heat treated at 100, 550, 1,000, 1,300 and 1,600°C under dry air and argon atmospheres. Characterisation analyses showed that alumina coated SiC particles underwent to slightly internal oxidation with the partial oxidation depending on the oxygen ion mobility during the transformation from boehmite to alumina in the interior atmosphere while alumina coated SiC particles gained oxidation resistance against the outer atmosphere.

The currently used measures of retinal function are limited by being subjective, nonlocalized, or taxing for patients. To address these limitations, we sought to develop and evaluate a deep learning (DL) method to automatically predict the functional end point (retinal sensitivity) based on structural OCT images.