AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

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.

Linking Function and Structure with ReSensNet: Predicting Retinal Sensitivity from OCT using Deep Learning.

PURPOSE: 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. DESIGN: Retrospective, cross-sectional study. SUBJECTS: In total, 714 volumes of 289 patients were used in this study. METHODS: A DL algorithm was developed to automatically predict a comprehensive retinal sensitivity map from an OCT volume. Four hundred sixty-three spectral-domain OCT volumes from 174 patients and their corresponding microperimetry examinations (Nidek MP-1) were used for development and internal validation, with a total of 15 563 retinal sensitivity measurements. The patients presented with a healthy macula, early or intermediate age-related macular degeneration, choroidal neovascularization, or geographic atrophy. In addition, an external validation was performed using 251 volumes of 115 patients, comprising 3 different patient populations: those with diabetic macular edema, retinal vein occlusion, or epiretinal membrane. MAIN OUTCOME MEASURES: We evaluated the performance of the algorithm using the mean absolute error (MAE), limits of agreement (LoA), and correlation coefficients of point-wise sensitivity (PWS) and mean sensitivity (MS). RESULTS: The algorithm achieved an MAE of 2.34 dB and 1.30 dB, an LoA of 5.70 and 3.07, a Pearson correlation coefficient of 0.66 and 0.84, and a Spearman correlation coefficient of 0.68 and 0.83 for PWS and MS, respectively. In the external test set, the method achieved an MAE of 2.73 dB and 1.66 dB for PWS and MS, respectively. CONCLUSIONS: The proposed approach allows the prediction of retinal function at each measured location directly based on an OCT scan, demonstrating how structural imaging can serve as a surrogate of visual function. Prospectively, the approach may help to complement retinal function measures, explore the association between image-based information and retinal functionality, improve disease progression monitoring, and provide objective surrogate measures for future clinical trials.

Fundus autofluorescence and optical coherence tomography biomarkers associated with the progression of geographic atrophy secondary to age-related macular degeneration.

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.

SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY, FLUORESCEIN ANGIOGRAPHY, AND INDOCYANINE GREEN ANGIOGRAPHY COMPARISONS REVISITED: Using a Novel Deep-Learning-Assisted Approach for Image Registration.

PURPOSE: To compare area measurements between swept source optical coherence tomography angiography (SSOCTA), fluorescein angiography (FA), and indocyanine green angiography (ICGA) after applying a novel deep-learning-assisted algorithm for accurate image registration. METHODS: We applied an algorithm for the segmentation of blood vessels in FA, ICGA, and SSOCTA images of 24 eyes with treatment-naive neovascular age-related macular degeneration. We trained a model based on U-Net and Mask R-CNN for each imaging modality using vessel annotations and junctions to estimate scaling, translation, and rotation. For fine-tuning of the registration, vessels and the elastix framework were used. Area, perimeter, and circularity measurements were performed manually using ImageJ. RESULTS: Choroidal neovascularization lesion size, perimeter, and circularity delineations showed no significant difference between SSOCTA and ICGA (all P > 0.05). Choroidal neovascularization area showed excellent correlation between SSOCTA and ICGA (r = 0.992) and a Bland-Altman bias of -0.10 ± 0.24 mm. There was no significant difference in foveal avascular zone size between SSOCTA and FA (P = 0.96) and an extremely small bias of 0.0004 ± 0.04 mm and excellent correlation (r = 0.933). Foveal avascular zone perimeter was not significantly different, but foveal avascular zone circularity was significantly different (P = 0.047), indicating that some small cavities or gaps may be missed leading to higher circularity values representing a more round-shaped foveal avascular zone in FA. CONCLUSION: We found no statistically significant differences between SSOCTA and FA and ICGA area measurements in patients with treatment-naive neovascular age-related macular degeneration after applying a deep-learning-assisted approach for image registration. These findings encourage a paradigm shift to using SSOCTA as a first-line diagnostic tool in neovascular age-related macular degeneration.

Power regulation and electromigration in platinum microwires.

We introduce a new experimental setup with a biasing circuit and computer control for electrical power regulation under reversing polarity in Pt microwires with dimensions of 1×10 µm(2). The circuit is computer controlled via a data acquisition board. It amplifies a control signal from the computer and drives current of alternating polarity through the sample in question. Time-to-failure investigations under DC and AC current stress are performed. We confirm that AC current stress can improve the life time of microwires at least by a factor of 10(3) compared to the corresponding time-to-failure under DC current stress.