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Animals need to cope with abundant sensory information, and one strategy is to selectively direct attention to only the most relevant part of the environment. Although the cortical networks of selective attention have been studied extensively, its underlying neurotransmitter systems, especially the role of the inhibitory neurotransmitter gamma-aminobutyric acid (GABA), remain less well understood. Increased GABAA receptor activity because of administration of benzodiazepines such as lorazepam is known to slow reactions in cognitive tasks. However, there is limited knowledge about GABAergic involvement in selective attention. Particularly, it is unknown whether increased GABAA receptor activity slows the build-up of selectivity or generally widens attentional focus. To address this question, participants (n = 29) received 1 mg lorazepam and placebo (within-subjects, double-blind) and performed an extended version of the flanker task. The spatial distribution of selective attention was studied by systematically manipulating number and position of incongruent flankers; the temporal build-up was characterized using delta plots. An online task version was presented to an independent, unmedicated sample (n = 25) to verify task effects. Under placebo and in the unmedicated sample, only the number of incongruent flankers, but not their position, influenced RTs. Incongruent flankers impaired RTs more strongly under lorazepam than placebo, especially when adjacent to the target. Delta plot analyses of RT showed that this effect persisted even when participants reacted slowly, indicating that lorazepam-induced impairments in selective attention do not result from simply slowed down build-up of selectivity. Instead, our data indicate that increased GABAA receptor activity widens the attentional focus.

To quantify the impact of the mode of administration (MOA) on scores of the Vision Impairment in Low Luminance (VILL) questionnaire.The VILL questionnaire was implemented using different MOAs (paper, interview, electronic), in addition to a demographical survey of adult participants recruited at an outpatient eye clinic, with the initial MOA being either paper or interview. Polytomous Rasch models were used to generate person measure scores for the three subscales of the VILL questionnaire (reading, VILL_R; mobility, VILL_M; and emotional, VILL_E). Measures of agreement among the different MOAs were calculated (self-administered paper/interview, self-administered paper/self-administered electronic, and interview/self-administered electronic). An age-matched analysis was performed to control for the impact of the initial MOA, administration interval, visual acuity, and self-reported hearing difficulties.We included 309 participants (mean age, 63 ± 14 years; 61% female). Intra-class correlation coefficients were 0.930, 0.919, and 0.799 for paper versus interview assessment; 0.951, 0.959, and 0.916 for paper versus electronic; and 0.967, 0.955, and 0.907 for interview versus electronic assessment (VILL_R, VILL_M, and VILL_E, respectively). Mean differences were 0.35, 0.41, and 1.74 logits; 0.32, 0.18, and 0.68 logits; and 0.08, 0.22, and 0.63 logits, respectively. None of the mentioned factors significantly affected the results (corrected P ≥ 0.11).Paper, interview, and electronic MOAs of the VILL can be considered equivalent. Reporting across the main MOAs of self-administration (paper) and interviewer-administration was unaffected by better eye visual acuity and self-reported hearing difficulties.The results support use of the VILL questionnaire with flexible modes of administration.

Early detection and treatment can prevent irreversible blindness from diabetic retinopathy (DR), which is the leading cause of visual impairment among working-aged adults worldwide. Some 80% of affected persons live in low- and middle-income countries, yet lack of resources has largely prevented DR screening implementation in these world regions. Smartphone-based fundus imaging (SBFI) allows for low-cost mobile fundus examination using an adapter on a smartphone; however, key aspects such as image quality, diagnostic accuracy, and comparability of different approaches have not been systematically assessed to date.Evaluation of diagnostic technology.A total of 381 eyes of 193 patients with diabetes were recruited at outreach eye clinics in South India.We compared 4 technically different approaches of SBFI (3 approaches based on direct and 1 approach based on indirect ophthalmoscopy) in terms of image quality and diagnostic accuracy for DR screening.Image quality (sharpness/focus, reflex artifacts, contrast, and illumination), field-of-view, examination time, and diagnostic accuracy for DR screening were analyzed against conventional fundus photography and clinical examination.Smartphone-based fundus imaging based on indirect ophthalmoscopy yielded the best image quality (P0.01), the largest field-of-view, and the longest examination time (111 vs. 68-86 seconds, P0.0001). Agreement with the reference standard (Cohen's kappa 0.868) and sensitivity/specificity to detect DR were highest for the indirect SBFI approach (0.79/0.99 for any DR and 1.0/1.0 for severe DR, 0.79/1.0 for diabetic maculopathy).Smartphone-based fundus imaging can meet DR screening requirements in an outreach setting; however, not all devices are suitable in terms of image quality and diagnostic accuracy. Smartphone-based fundus imaging might aid in alleviating the burden of DR screening in low- and middle-income countries, and these results will allow for a better selection of SBFI devices in field trials for DR screening.

BACKGROUND Empirical models have been an integral part of everyday clinical practice in ophthalmology since the introduction of the Sanders-Retzlaff-Kraff (SRK) formula. Recent developments in the field of statistical learning (artificial intelligence, AI) now enable an empirical approach to a wide range of ophthalmological questions with an unprecedented precision. OBJECTIVE Which criteria must be considered for the evaluation of AI-related studies in ophthalmology? MATERIAL AND METHODS Exemplary prediction of visual acuity (continuous outcome) and classification of healthy and diseased eyes (discrete outcome) using retrospectively compiled optical coherence tomography data (50 eyes of 50 patients, 50 healthy eyes of 50 subjects). The data were analyzed with nested cross-validation (for learning algorithm selection and hyperparameter optimization). RESULTS Based on nested cross-validation for training, visual acuity could be predicted in the separate test data-set with a mean absolute error (MAE, 95% confidence interval, CI of 0.142 LogMAR [0.077; 0.207]). Healthy versus diseased eyes could be classified in the test data-set with an agreement of 0.92 (Cohen's kappa). The exemplary incorrect learning algorithm and variable selection resulted in an MAE for visual acuity prediction of 0.229 LogMAR [0.150; 0.309] for the test data-set. The drastic overfitting became obvious on comparison of the MAE with the null model MAE (0.235 LogMAR [0.148; 0.322]). CONCLUSION Selection of an unsuitable measure of the goodness-of-fit, inadequate validation, or withholding of a null or reference model can obscure the actual goodness-of-fit of AI models. The illustrated pitfalls can help clinicians to identify such shortcomings.

Multimodale Bildgebung kann die Netzhaut in bisher unerreichtem Detail abbilden, und die gemeinsame Analyse (Integration) dieser Daten ermoglicht nicht nur die Sicherung von Diagnosen, sondern auch deren Prazisierung. Menschen stosen jedoch bei der Analyse dieser Informationsmenge an zeitliche und kognitive Grenzen, sodass das Potenzial einer gemeinsamen Betrachtung der Befunde bisher weitgehend ungenutzt ist. Automatische Bildverarbeitung und Methoden, die unter dem Sammelbegriff der kunstlichen Intelligenz (KI) zusammengefasst werden, sind in der Lage den Flaschenhals bei der Auswertung zu uberwinden und das volle Potenzial der vorliegenden Daten auszuschopfen. Ein grundlegendes Verstandnis von KI-Methoden und die Fahigkeit, diese einzusetzen, werden fur Ophthalmologen in Zukunft entsprechend immer wichtiger werden. In diesem Beitrag geben wir einen Einblick in die Funktionsweise von KI-Methoden und den aktuellen Stand der Forschung im Bereich der automatischen Bildauswertung.

With the advent of smartphone-based fundus imaging (SBFI), a low-cost alternative to conventional digital fundus photography has become available. SBFI allows for a mobile fundus examination, is applicable both with and without pupil dilation, comes with built-in connectivity and post-processing capabilities, and is relatively easy to master. Furthermore, it is delegable to paramedical staff/technicians and, hence, suitable for telemedicine. Against this background a variety of SBFI applications have become available including screening for diabetic retinopathy, glaucoma, and retinopathy of prematurity and its applications in emergency medicine and pediatrics. In addition, SBFI is convenient for teaching purposes and might serve as a surrogate for direct ophthalmoscopy. First wide-field montage techniques are available and the combination of SBFI with machine learning algorithms for image analyses is promising. In conclusion, SBFI has the potential to make fundus examinations and screenings for patients particularly in low- and middle-income settings more accessible and, therefore, aid tackling the burden of diabetic retinopathy, glaucoma, and retinopathy of prematurity screening. However, image quality for SBFI varies substantially and a reference standard for grading appears prudent. In addition, there is a strong need for comparison of different SBFI approaches in terms of applicability to disease screening and cost-effectiveness.

PURPOSE: To evaluate the secondary and exploratory outcomes of the Laser Intervention in Early Stages of Age-Related Macular Degeneration (LEAD) study, a 36-month trial of a subthreshold nanosecond laser (SNL) treatment for slowing the progression to late age-related macular degeneration (AMD) in its early stages. DESIGN: Multicenter, randomized, sham-controlled trial. PARTICIPANTS: Two-hundred ninety-two patients with bilateral large drusen. METHODS: Participants were randomly assigned to receive SNL or sham treatment to the study eye at 6-month intervals. MAIN OUTCOME MEASURES: The secondary outcome measure of the LEAD study was the time to development of late AMD, defined by multimodal imaging in the non-study eye. The exploratory outcome measures were the rate of change in best-corrected visual acuity (BCVA), low-luminance visual acuity, microperimetric mean sensitivity, drusen volume in the study and non-study eyes, and participant-reported outcomes based on the Night Vision Questionnaire and Impact of Vision Impairment questionnaire. RESULTS: Progression to late AMD in the non-study eye was not significantly delayed with SNL treatment (hazard ratio, 0.83; 95% confidence interval, 0.40-1.71; P = 0.611). There was no evidence of effect modification based on the coexistence of reticular pseudodrusen; interaction P = 0.065). There was no significant difference between study groups in the rate of change of low-luminance visual acuity, microperimetric mean sensitivity, and drusen volume in the study or non-study eyes, and Night Vision Questionnaire and Impact of Vision Impairment questionnaire scores (all P ≥ 0.167). The rate of BCVA decline was slightly higher for participants in the SNL group compared with the sham treatment group in the study eye (-0.54 and 0.23 letters/year, respectively; P < 0.001) but not the non-study eye (-0.48 and -0.56 letters/year, respectively; P = 0.628). CONCLUSIONS: Subthreshold nanosecond laser treatment of one eye did not have an effect on delaying progression to late AMD in the fellow eye and did not, in general, have an impact on the exploratory structural, functional, and participant-reported outcomes.

Smartphone-based fundus imaging (SBFI) is an innovative and low-cost alternative for color fundus photography. Since the first reports on this topic more than 10 years ago a large number of studies on different adapters and clinical applications have been published.The aim of this review article is to provide an overview on the development of SBFI and adapters and clinical applications published so far.A literature search was performed using the MEDLINE and Science Citation Index Expanded databases without time restrictions.Overall, 11 adapters were included and compared in terms of exemplary image material, field of view, acquisition costs, weight, software, application range, smartphone compatibility and certification. Previously published SBFI applications are screening for diabetic retinopathy, glaucoma and retinopathy of prematurity as well as the application in emergency medicine, pediatrics and medical education/teaching. Image quality of conventional retinal cameras is in general superior to SBFI. First approaches on automatic detection of diabetic retinopathy through SBFI are promising and the use of automatic image processing algorithms enables the generation of wide-field image montages.SBFI is a versatile, mobile, low-cost alternative to conventional equipment for color fundus photography. In addition, it facilitates the delegation of ophthalmological examinations to assistance personnel in telemedical settings, could simplify retinal documentation, improve teaching, and improve ophthalmological care, particularly in countries with low and middle incomes.HINTERGRUND: Die Smartphone-basierte Fundusfotografie (SBF) stellt eine innovative und kostengünstige Möglichkeit zur Bildgebung des Augenhintergrundes dar. Seit den ersten Anwendungsberichten von vor über 10 Jahren wurde eine Vielzahl an Studien zu unterschiedlichen Adaptern und klinischen Anwendungen publiziert.Ziel dieser Arbeit ist es, einen Überblick zur Entwicklung der SBF, zu bisherigen Anwendungsbereichen und Adaptern zu geben.Es wurde eine Literaturrecherche in den Datenbanken MEDLINE und Science Citation Index Expanded ohne zeitliche Einschränkung durchgeführt.Elf Adapter wurden eingeschlossen und hinsichtlich beispielhaftem Bildmaterial, dargestellten Fundusbereich, Anschaffungskosten, Gewicht, Software, Anwendungsbereich, Smartphone-Kompatibilität und Zertifizierung verglichen. Bislang publizierte Anwendungsbereiche der SBF sind das Screening auf diabetische Retinopathie, Glaukom und Frühgeborenenretinopathie sowie ein Einsatz in der Notfallmedizin, Pädiatrie und medizinischen Lehre. Die Bildqualität konventioneller Geräte ist der SBF im Allgemeinen überlegen. Erste Ansätze zur automatischen Detektion diabetischer Retinopathie auf Smartphone-basierten Fundusbildern sind vielversprechend. Der Einsatz automatischer Bildverarbeitungsalgorithmen ermöglicht die Erstellung von Weitwinkelaufnahmen.Die SBF ist eine vielseitig einsetzbare, mobile und kostengünstige Alternative zu konventionellen Geräten für die Fundusfotografie. Darüber hinaus eignet sie sich im Rahmen telemedizinischer Ansätze zur Delegation einfacher ophthalmologischer Untersuchungen an Hilfspersonal und könnte zur Vereinfachung von Befunddokumentation, Verbesserung der Lehre und v. a. in Ländern mit niedrigem/mittlerem Einkommen zur Verbesserung der augenärztlichen Versorgung beitragen.

Convolutional neural networks (CNNs) represent the state of the art for fully automated medical image segmentation. However, few works have combined CNNs with interactive user feedback in order to verify and, where necessary, correct their results. We present an interactive visual system that achieves this for the specific use case of segmenting drusen, which serve as a biomarker of age related macular degeneration, from Optical Coherence Tomography. Our main idea is to exploit the probabilistic nature of CNN-based segmentation. First, we derive two uncertainty measures from it. We demonstrate that they indicate cases in which automated segmentation is likely to have failed, and that visualizing them makes manual verification and correction more efficient. Second, based on the probabilistic information, we design intelligent tools for segmentation correction, which automatically propose the most likely alternative segmentation in agreement with user-specified constraints. In a small user study, uncertainty visualization and intelligent interaction reduced the time required to correct retinal layer segmentation by around 53% and, for drusen segmentation, even by 73%. In the future, we plan to use our system not only for efficient segmentation correction, but also for rapid creation of larger training sets.

The purpose of this study was to assess which visual function measures are most strongly associated with vision-related quality of life (VRQoL) in age-related macular degeneration (AMD). A cross-sectional study of subjects with early AMD (n = 10), intermediate AMD (n = 42) and late AMD (n = 38) was conducted. Subjects were interviewed with the Impact of Vision Impairment (IVI) questionnaire. Functional tests performed included best-corrected visual acuity (BCVA), low luminance visual acuity (LLVA), visual acuity measured with the Moorfields Acuity Charts (MAC), contrast sensitivity, reading speed, mesopic and dark-adapted microperimetry. The relationship between VRQoL and visual function was assessed with multiple regressions controlling for confounders. Rasch analysis demonstrated the validity of the IVI to assess VRQoL through three subscales: reading and accessing information, mobility and independence, and emotional well-being. Subjects with late AMD had significant lower IVI scores on all subscales compared with intermediate and early AMD (p

Background: Screening for diabetic eye disease (DED) and general diabetes care is often separate, which leads to delays and low adherence to DED screening recommendations. Thus, we assessed the feasibility, achieved image quality, and possible barriers of telemedical DED screening in a point-of-care general practice setting and the accuracy of an automated algorithm for detection of DED.Methods: Patients with diabetes were recruited at general practices. Retinal images were acquired using a non-mydriatic camera (CenterVue, Italy) by medical assistants. Images were quality assessed and double graded by two graders. All images were also graded automatically using a commercially available artificial intelligence (AI) algorithm (EyeArt version 2.1.0, Eyenuk Inc.).Results: A total of 75 patients (147 eyes; mean age 69 years, 96% type 2 diabetes) were included. Most of the patients (51; 68%) preferred DED screening at the general practice, but only twenty-four (32%) were willing to pay for this service. Images of 63 patients (84%) were determined to be evaluable, and DED was diagnosed in 6 patients (8.0%). The algorithm's positive/negative predictive values (95% confidence interval) were 0.80 (0.28-0.99)/1.00 (0.92-1.00) and 0.75 (0.19-0.99)/0.98 (0.88-1.00) for detection of any DED and referral-warranted DED, respectively.Overall, the number of referrals was 18 (24%) for manual telemedical assessment and 31 (41%) for the artificial intelligence (AI) algorithm, resulting in a relative increase of referrals by 72% when using AI.Conclusions: Our study shows that achieved overall image quality in a telemedical GP-based DED screening was sufficient and that it would be accepted by medical assistants and patients in most cases. However, good image quality and integration into existing workflow remain challenging. Based on these findings, a larger-scale implementation study is warranted.

The Acknowledgements section in the original version of this Article was incomplete. ""This research was supported by the Else Kröner-Fresenius Foundation/German Scholars Organization (EKFS/GSO 16) to RF, the BONFOR GEROK Program, Faculty of Medicine, University of Bonn, (Grant No. O-137.0028) to MW, the GEROK Program, Faculty of Medicine, University of Bonn, (Grant No. O-137.0026) to ST and the German Ministry of Education and Research (BMBF), FKZ 13N10349. The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report."" now reads: ""This research was supported by the Else Kröner-Fresenius Foundation/German Scholars Organization (EKFS/GSO 16) to RF, the BONFOR GEROK Program, Faculty of Medicine, University of Bonn, (Grant No. O-137.0028) to MW, the GEROK Program, Faculty of Medicine, University of Bonn, (Grant No. O-137.0026) to ST and the German Ministry of Education and Research (BMBF), FKZ 13N10349. The work of Shekoufeh Gorgi Zadeh was supported by a grant from Deutsche Forschungsgemeinschaft (DFG), grant number SCHM 2966/2-1. The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report."" The original Article has been corrected.

Empirical models have been an integral part of everyday clinical practice in ophthalmology since the introduction of the Sanders-Retzlaff-Kraff (SRK) formula. Recent developments in the field of statistical learning (artificial intelligence, AI) now enable an empirical approach to a wide range of ophthalmological questions with an unprecedented precision.Which criteria must be considered for the evaluation of AI-related studies in ophthalmology?Exemplary prediction of visual acuity (continuous outcome) and classification of healthy and diseased eyes (discrete outcome) using retrospectively compiled optical coherence tomography data (50 eyes of 50 patients, 50 healthy eyes of 50 subjects). The data were analyzed with nested cross-validation (for learning algorithm selection and hyperparameter optimization).Based on nested cross-validation for training, visual acuity could be predicted in the separate test data-set with a mean absolute error (MAE, 95% confidence interval, CI of 0.142 LogMAR [0.077; 0.207]). Healthy versus diseased eyes could be classified in the test data-set with an agreement of 0.92 (Cohen's kappa). The exemplary incorrect learning algorithm and variable selection resulted in an MAE for visual acuity prediction of 0.229 LogMAR [0.150; 0.309] for the test data-set. The drastic overfitting became obvious on comparison of the MAE with the null model MAE (0.235 LogMAR [0.148; 0.322]).Selection of an unsuitable measure of the goodness-of-fit, inadequate validation, or withholding of a null or reference model can obscure the actual goodness-of-fit of AI models. The illustrated pitfalls can help clinicians to identify such shortcomings.HINTERGRUND: Empirische Modelle sind seit Einführung der SRK(Sanders-Retzlaff-Kraff)-Formel im klinischen Alltag der Augenheilkunde etabliert. Rezente Entwicklungen im Bereich des statistischen Lernens („künstliche Intelligenz“ [KI]) ermöglichen jetzt ein empirisches Vorgehen für vielfältigste ophthalmologische Fragestellungen bei bislang unerreichter Präzision.Welche Kriterien müssen für die Bewertung von Arbeiten zum Thema KI in der Augenheilkunde berücksichtigt werden?Es erfolgen die beispielhafte Vorhersage des Visus (stetige Zielgröße) und Klassifikation von gesunden und kranken Augen (diskrete Zielgröße) anhand von retrospektiven optischen Kohärenztomographiebilddaten (50 Augen von 50 Patienten, 50 gesunde Augen von 50 Probanden). Die Daten wurden mit verschachtelter Kreuzvalidierung (zur Lernalgorithmusauswahl und Hyperparameteroptimierung) analysiert.Durch verschachtelte Kreuzvalidierung ließ sich der Visus im separaten Testdatensatz mit einem mittleren absoluten Fehler (MAE, [95 %-CI, Konfidenzintervall]) von 0,142 LogMAR [0,077; 0,207] vorhersagen. Kranke und gesunde Augen ließen sich im Testdatensatz mit einer Konkordanz von (Kappa nach Cohen) 0,92 klassifizieren. Die beispielhafte inkorrekte Lernalgorithmus- und Variablenauswahl resultierte in einem MAE von 0,229 LogMAR [0,150; 0,309] für den Testdatensatz. Erst durch Vergleich mit dem MAE des Nullmodells (0,235 LogMAR [0,148; 0,322]) wurde die Überanpassung offensichtlich.Die Auswahl einer ungeeigneten Kennzahl für die Anpassungsgüte, inadäquate Validierung oder Unterschlagen eines Null- oder Referenzmodells kann die tatsächliche Anpassungsgüte von KI-Modellen verschleiern. Die illustrierten Fallstricke können Klinikern und Forschern helfen, solche Unzulänglichkeiten zu erkennen.

With the advent of smartphone-based fundus imaging (SBFI), a low-cost alternative to conventional digital fundus photography has become available. SBFI allows for a mobile fundus examination, is applicable both with and without pupil dilation, comes with built-in connectivity and post-processing capabilities, and is relatively easy to master. Furthermore, it is delegable to paramedical staff/technicians and, hence, suitable for telemedicine. Against this background a variety of SBFI applications have become available including screening for diabetic retinopathy, glaucoma, and retinopathy of prematurity and its applications in emergency medicine and pediatrics. In addition, SBFI is convenient for teaching purposes and might serve as a surrogate for direct ophthalmoscopy. First wide-field montage techniques are available and the combination of SBFI with machine learning algorithms for image analyses is promising. In conclusion, SBFI has the potential to make fundus examinations and screenings for patients particularly in low- and middle-income settings more accessible and, therefore, aid tackling the burden of diabetic retinopathy, glaucoma, and retinopathy of prematurity screening. However, image quality for SBFI varies substantially and a reference standard for grading appears prudent. In addition, there is a strong need for comparison of different SBFI approaches in terms of applicability to disease screening and cost-effectiveness.

Quantifying intraocular inflammation is crucial in managing uveitis patients. We assessed the minimum B-scan density for reliable automated vitreous intensity (VI) assessment, using a novel approach based on optical coherence tomography (OCT). OCT volume scans centered on the macula were retrospectively collected in patients with uveitis. Nine B-scans per volume scan at fixed locations were automatically analyzed. The following B-scan selections were compared against the average score of 9 B-scans per volume scan as a reference standard: 1/3/5/7 central scans (1c/3c/5c/7c), 3 widely distributed scans (3w). Image data of 49 patients (31 females) were included. The median VI was 0.029 (IQR: 0.032). The intra-class-correlation coefficient of the VI across the 9 B-scans was 0.923. The median difference from the reference standard ranged between 0.001 (7c) and 0.006 (1c). It was significantly lower for scan selection 3w than 5c, p(adjusted) = 0.022, and lower for selection 7c than 3w, p(adjusted) = 0.003. The scan selections 7c and 3w showed the two highest areas under the receiver operating curve (0.985 and 0.965, respectively). Three widely distributed B-scans are sufficient to quantify VI reliably. Highest reliability was achieved using 7 central B-scans. Automated quantification of VI in uveitis is reliable and requires only few OCT B-scans.

Purpose To investigate characteristics of the foveal pit and the foveal avascular zone (FAZ) in patients with Alport syndrome (AS), a rare monogenetic disease due to mutations in genes encoding for collagen type IV. Methods Twenty-eight eyes of nine patients with AS, and five autosomal-recessive carriers and 15 eyes from 15 age-similar healthy control subjects were examined using optical coherence tomography (OCT) and OCT-angiography (OCT-A). Foveal configuration and FAZ measures including the FAZ area, circularity, and vessel density in the central 1° and 3° were correlated. Results Foveal hypoplasia was found in 10 eyes from seven patients with either genotype. In contrast, a staircase foveopathy was found in seven eyes of four X-linked AS patients. The average FAZ area did not differ significantly between AS patients and control subjects (mean ± SD 0.24 ± 0.24 mm2 vs. 0.21 ± 0.09 mm2; P = 0.64). Five eyes showed absence or severe anomalies of the FAZ with crossing macular capillaries that was linked to the degree of foveal hypoplasia on OCT images leading to a significant inverse correlation of FAZ area and foveal thickness (r = -0.88; P < 0.001). In contrary, female patients with X-linked mutations exhibited a significantly greater FAZ area (0.48 ± 0.30 mm2 vs. 0.21 ± 0.09 mm2; P = 0.007), in line with OCT findings of a staircase foveopathy. Conclusions The foveal phenotypic spectrum in AS ranges from foveal hypoplasia and absence of a FAZ to staircase foveopathy with an enlarged FAZ. Because the development of the FAZ and foveal pit are closely related, these findings suggest an important role for collagen type IV in foveal development and maturation.

Scientific reports 10(1), 7395 (2020). doi:10.1038/s41598-020-63924-6, Published by Macmillan Publishers Limited, part of Springer Nature, [London]

Introduction For quantification of Optical Coherence Tomography Angiography (OCTA) images, Vessel Density (VD) and Vessel Skeleton Density (VSD) are well established parameters and different algorithms are in use for their calculation. However, comparability, reliability and ability to discriminate healthy and impaired macular perfusion of different algorithms are unclear, yet, of potential high clinical relevance. Hence, we assessed comparability and test-retest reliability of the most common approaches. Materials and methods Two consecutive 3×3mm OCTA en face images of the superficial and deep retinal layer were acquired with swept-source OCTA. VD and VSD were calculated with manual thresholding and six automated thresholding algorithms (Huang, Li, Otsu, Moments, Mean, Percentile) using ImageJ and compared in terms of intra-class correlation coefficients, measurement differences and repeatability coefficients. Receiver operating characteristic analyses (healthy vs. macular pathology) were performed and Area Under the Curve (AUC) values were calculated. Results Twenty-six eyes (8 female, mean age: 47 years) of 15 patients were included (thereof 15 eyes with macular pathology). Binarization thresholds, VD and VSD differed significantly between the algorithms and compared to manual thresholding (p < 0.0001). Inter-measurement differences did not differ significantly between patients with healthy versus pathologic maculae (p ≥ 0.685). Reproducibility was higher for the automated algorithms compared to manual thresholding on all measures of reproducibility assessed. AUC was significantly higher for the Mean algorithm compared to the manual approach with respect to the superficial retinal layer. Conclusions Automated thresholding algorithms yield a higher reproducibility of OCTA parameters and allow for a more sensitive diagnosis of macular pathology. However, different algorithms are not interchangeable nor results readily comparable. Especially the Mean algorithm should be investigated in further detail. Automated thresholding algorithms are preferable but more standardization is needed for clinical use.

Even though it is important to screen patients with diabetes for signs of diabetic retinopathy (DR), doing so comprehensively remains a practical challenge in low- and middle-income countries due to limited resources and financial constraints. Supervised machine learning has shown a strong potential for automated DR detection, but has so far relied on photographs that show all relevant parts of the fundus, which require relatively costly imaging systems. We present the first approach that automatically detects DR from fundus videos that show different parts of the fundus at different times, and that can be acquired with a low-cost smartphone-based fundus imaging system. Our novel image analysis pipeline consists of three main steps: Detecting the lens with a circle Hough Transform, detecting informative frames using a Support Vector Machine, and detecting the disease itself with an attention-based multiple instance learning (MIL) CNN architecture. Our results support the feasibility of a smartphone video based approach.

Purpose To investigate the involvement of the retinal and choriocapillaris microvasculature in intermediate uveitis on optical coherence tomography angiography (OCT-A). Design Case-control study. Methods Patients and age-matched controls were imaged with swept-source OCT-A. Using ImageJ, superficial and deep retinal vasculature were semi-automatically analyzed for vessel (VD) and skeleton density (SD), vessel diameter index (VDI), and fractal dimension (FD). Choriocapillaris layer was automatically graded for mean signal intensity, signal intensity standard deviation, kurtosis of signal intensity distribution, and flow signal voids. Results Twenty-nine intermediate uveitis eyes and 30 control eyes were included. Both superficial and deep retinal layers showed significant reduction in all OCT-A parameters (eg, superficial retinal layer: 0.31 vs 0.40 VD, 5.6e−8 vs 6.4e−8 SD, 5.4e6 vs 6.1e6 VDI, and 1.78 vs 1.79 FD, respectively, all P Conclusions In intermediate uveitis, reduced vascular density and complexity in superficial as well as deep retinal layers and altered choriocapillaris perfusion are present. Moreover, these findings indicate impairment of the macular microvasculature even in the absence of macular edema. The results of our study may aid in the diagnosis as well as the monitoring of intermediate uveitis.

BACKGROUND Smartphone-based fundus imaging allows for mobile and inexpensive fundus examination with the potential to revolutionize eye care, particularly in lower-resource settings. However, most smartphone-based fundus imaging adapters convey image quality not comparable to conventional fundus imaging. OBJECTIVE The purpose of this study was to evaluate a novel smartphone-based fundus imaging device for documentation of a variety of retinal/vitreous pathologies in a patient sample with wide refraction and age ranges. METHODS Participants’ eyes were dilated and imaged with the iC2 funduscope (HEINE Optotechnik) using an Apple iPhone 6 in single-image acquisition (image resolution of 2448 × 3264 pixels) or video mode (1248 × 1664 pixels) and a subgroup of participants was also examined by conventional fundus imaging (Zeiss VISUCAM 500). Smartphone-based image quality was compared to conventional fundus imaging in terms of sharpness (focus), reflex artifacts, contrast, and illumination on semiquantitative scales. RESULTS A total of 47 eyes from 32 participants (age: mean 62.3, SD 19.8 years; range 7-93; spherical equivalent: mean –0.78, SD 3.21 D; range: –7.88 to +7.0 D) were included in the study. Mean (SD) visual acuity (logMAR) was 0.48 (0.66; range 0-2.3); 30% (14/47) of the eyes were pseudophakic. Image quality was sufficient in all eyes irrespective of refraction. Images acquired with conventional fundus imaging were sharper and had less reflex artifacts, and there was no significant difference in contrast and illumination (PP=.03, and P=.10, respectively). When comparing image quality at the posterior pole, the mid periphery, and the far periphery, glare increased as images were acquired from a more peripheral part of the retina. Reflex artifacts were more frequent in pseudophakic eyes. Image acquisition was also possible in children. Documentation of deep optic nerve cups in video mode conveyed a mock 3D impression. CONCLUSIONS Image quality of conventional fundus imaging was superior to that of smartphone-based fundus imaging, although this novel smartphone-based fundus imaging device achieved image quality high enough to document various fundus pathologies including only subtle findings. High-quality smartphone-based fundus imaging might represent a mobile alternative for fundus documentation in clinical practice.

Retinopathy of prematurity (ROP) is a frequent cause of treatable childhood blindness. The current dependency of telemedicine-based ROP screening on cost-intensive equipment does not meet the needs in economically disadvantaged regions. Smartphone-based fundus imaging (SBFI) allows for affordable and mobile fundus examination and, therefore, could facilitate cost-effective telemedicine-based ROP screening in low-resources settings. We compared non-contact SBFI and conventional contact fundus imaging (CFI) in terms of feasibility for ROP screening and documentation. Twenty-six eyes were imaged with both SBFI and CFI. Field-of-view was smaller (ratio of diameters, 1:2.5), level of detail was equal, and examination time was longer for SBFI as compared to CFI (109.0 ± 57.8 vs. 75.9 ± 36.3 seconds, p

Zur Behandlung der neovaskularen altersabhangigen Makuladegeneration (nAMD) stehen Behandlungsstrategien mit kontinuierlichem und bedarfsorientiertem („pro re nata“ [PRN]) Injektionsschema zur Verfugung. Bislang wurde das PRN-Behandlungsschema mit Einzelinjektionen bei Reaktivierung nicht mit dem PRN-Behandlungsschema mit Dreierinjektionen bei Reaktivierung (IVAN-Schema) verglichen. Vergleich der beiden nAMD-PRN-Behandlungsschemata mit Einzel- und Dreierinjektionen hinsichtlich Behandlungsergebnissen und -intensitat unter klinischen Routinebedingungen in einer retrospektiven Fallserie in 2 deutschen Behandlungszentren. Behandlungsnaive nAMD-Patienten, bei denen eine Therapie mit Einzel- oder Dreierinjektionsschema initiiert wurde, wurden eingeschlossen. Endpunkte waren bestkorrigierter Visus (LogMAR), zentrale Netzhautdicke in der optischen Koharenztomographie (μm) und Anzahl der Injektionen. Dies wurde jeweils fur die Zeitpunkte 3, 6, 12, 18 und 24 Monate nach Therapiebeginn analysiert. 146 Patienten mit Einzel- und 148 Patienten mit Dreierinjektionsschema wurden eingeschlossen. Nach 24 Monaten gab es weder beim bestkorrigierten Visus (Einzel- vs. Dreierinjektionsschema: 0,50 ± 0,42 vs. 0,56 ± 0,42, p = 0,14) noch bei der zentralen Netzhautdicke (303 ± 76,2 vs. 306 ± 110, p = 0,79) einen signifikanten Unterschied zwischen den beiden Behandlungsschemata. Auch die Anzahl der Injektionen unterschied sich nicht signifikant zwischen beiden Gruppen (13 ± 4,4 vs. 12 ± 5,4, p = 0,31). Dies war fur alle betrachteten Zeitpunkte der Fall. Die PRN-Behandlungsschemata mit Einzel- und Dreierinjektionen unterscheiden sich nach 24 Monaten nicht hinsichtlich funktioneller und struktureller Behandlungsergebnisse und sind entsprechend als aquivalent zu bewerten. Zur Beurteilung des langfristigen Therapieerfolges uber 24 Monate hinaus sind weitere Studien notwendig.

Entscheidende Voraussetzungen fur eine erfolgreiche Therapie der neovaskularen altersabhangigen Makuladegeneration (nvAMD) sind ein fruher Therapiestart und regelmasige Kontrollen. Jedoch kommt es haufig zu einer verzogerten Injektionsinitiierung oder einem nicht medizinisch indizierten Therapieabbruch, was die Therapieeffizienz verringert. Identifikation der Grunde fur Verzogerung des Therapiestarts sowie fur einen nicht medizinisch indizierten Therapieabbruch. Patienten, die eine neue Therapie begonnen haben, und solche, die eine Therapie selbststandig abgebrochen haben, wurden telefonisch jeweils mit einem standardisierten Fragebogen interviewt. Die Ergebnisse der Befragung wurden deskriptiv zusammengefasst. Es wurden 100 Patienten, bei denen eine Therapie initiiert wurde, und 55 Therapieabbrecher befragt. Bei Therapiestart kam es durchschnittlich zu einer Verzogerung von 22 (±28 SD) Tagen. Diese war grostenteils durch die Zeit bis zur Entschlussfindung, einen Augenarzt aufzusuchen, bedingt. Folgende Hauptgrunde wurden fur den Therapieabbruch angegeben: Transportprobleme (27 %), allgemein schlechter Gesundheitszustand (25 %) und die Annahme, die Therapie bringe nichts (11 %). 63 % der Therapieabbrecher gaben an, grundsatzlich eine Therapie wieder aufnehmen zu wollen. Bei der Versorgung von nvAMD-Patienten gibt es sowohl beim Therapiestart als auch bei der Therapieaufrechterhaltung Verbesserungspotenzial. Sensibilisierung fur die initialen Symptome einer nvAMD ist wichtig ebenso wie die Reduktion der Barrieren zur Therapieaufrechterhaltung, da die Mehrzahl der Abbrecher die Therapie gerne fortfuhren wurde.