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Purpose: To evaluate the utility of intraocular lens (IOL) power calculation using adjusted conventional keratometry (K) according to postoperative posterior to preoperative anterior corneal curvature radii (PPPA) ratio for eyes with Fuch's dystrophy undergoing cataract surgery combined with Descemet membrane endothelial keratoplasty (triple DMEK)., Methods: A fictitious refractive index (FRI) was determined (Pentacam HR®) based on the PPPA ratio in 50 eyes undergoing triple DMEK. Adjusted corneal power was calculated in every eye using adjusted K values: K values determined by the IOLMaster were converted to adjusted anterior corneal radius using the mean FRI. Posterior corneal radius was calculated using the mean PPPA ratio. Adjusted corneal power was determined based on the calculated corneal radii and thick lens formula. Refractive errors calculated using the Haigis, SRK/T, and HofferQ formulae based on the adjusted corneal power were compared with those based on conventional K measurements., Results: Calculated PPPA ratio and FRI were 0.801 and 1.3271. Mean prediction error based on conventional K was in the hyperopic direction (Haigis: 0.84D; SRK/T: 0.74D; HofferQ: 0.74D) and significantly higher (P < 0.001) than that based on adjusted corneal power (0.18D, 0.22D, and 15D, respectively). When calculated according to adjusted corneal power, the percentage of eyes with a hyperopic shift > 0.5D fell significantly from 64 to 30% (Haigis), 62 to 36% (SRK/T), and 58 to 26% (HofferQ), respectively., Conclusion: IOL power calculation based on adjusted corneal power can be used to reduce the risk of a hyperopic shift after triple DMEK and provides a more accurate refractive outcome than IOL power calculation using conventional K., (© 2022. The Author(s).)

Background: The use of artificial intelligence (AI) interesting for automated image segmentation, analysis and classification, among others and has already been described for various fields of ophthalmology., Objective: This manuscript provides an overview of current approaches and advances in the application of big data and AI in various diseases of the optic nerve head., Material and Methods: A PubMed search was performed. Studies were searched for that answered clinical questions using big data approaches or classical machine learning methods in the analysis of multimodal imaging of the optic nerve head., Results: Big data can help to answer clinical questions in common diseases such as glaucoma. The AI is applied for the segmentation of multimodal imaging of the optic nerve head as well as for the classification of diseases, such as glaucoma or optic disc edema on this imaging data., Conclusion: With the help of big data and AI, relationships can be recognized more easily and the diagnostics and course assessment of diseases of the optic nerve head can be facilitated or automated. A prerequisite for clinical application is a CE marking as a medical device in Europe and approval by the Food and Drug Administration in the USA., (© 2021. Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: "Flipped classroom" is a didactic teaching concept in which learning contents are prepared by self-study with arranged tools before the classroom session. The concept offers the advantage of a uniform knowledge base for the students at the beginning of the course and also the advantage of a greater theoretical knowledge, which creates more opportunities for practical exercises, application and consolidation in the subsequent joint teaching units. This study describes the establishment and application of such a model in student teaching in ophthalmology and analyzes the student's evaluation., Methodology: For the winter term 2018/2019, a new teaching module was designed and established in a cooperation between the department of ophthalmology and the Institute for Education and Study Affairs (IfAS) at the medical faculty of the University of Münster. A uniform training of the lecturers as well as a preparation of the students for the restructuring took place. After the course the evaluation of the students was recorded and evaluated using a standardized online evaluation., Results: Between the winter semester 2018/2019 and the winter semester 2019/2020, an average of 112.3 ± 4.0 students were taught with the "flipped classroom" model. Of these 93.7% were able to give an assessment. In the previous semesters with the old teaching concept (summer semester 2015 to summer semester 2018), the average number of students was 115.4 ± 15.1 with an assessment rate of 93.3%. The new teaching concept achieved on average a better assessment than the old module., Conclusion: With a "flipped classroom" space and flexibility can be generated for a more individual course preparation and at the same time a higher practical part. Further studies are needed to analyze whether this also enables a sustainable transfer of knowledge.

Purpose: The present retrospective study was designed to test the hypothesis that the postoperative posterior to preoperative anterior corneal curvature radii (PPPA) ratio in eyes with Fuch's dystrophy undergoing Descemet membrane endothelial keratoplasty (DMEK) is significantly different to the posterior to anterior corneal curvature radii (PA) ratio in virgin eyes and therefore renders conventional keratometry (K) and the corneal power derived by it invalid for intraocular lens (IOL) power calculation., Methods: Measurement of corneal parameters was performed using Scheimpflug imaging (Pentacam HR, Oculus, Germany). In 125 eyes with Fuch's dystrophy undergoing DMEK, a fictitious keratometer index was calculated based on the PPPA ratio. The preoperative and postoperative keratometer indices and PA ratios were also determined. Results were compared to those obtained in a control group consisting of 125 eyes without corneal pathologies. Calculated mean ratios and keratometer indices were then used to convert the anterior corneal radius in each eye before DMEK to postoperative posterior and total corneal power. To assess the most appropriate ratio and keratometer index, predicted and measured powers were compared using Bland-Altman plots., Results: The PPPA ratio determined in eyes with Fuch's dystrophy undergoing DMEK was significantly different (P < 0.001) to the PA ratio in eyes without corneal pathologies. Using the mean PA ratio (0.822) and keratometer index (1.3283), calculated with the control group data to convert the anterior corneal radius before DMEK to power, leads to a significant (P < 0.001) underestimation of postoperative posterior negative corneal power (mean difference (∆ = - 0.14D ± 0.30) and overestimation of total corneal power (∆ = - 0.45D ± 1.08). The lowest prediction errors were found using the geometric mean PPPA ratio (0.806) and corresponding keratometer index (1.3273) to predict the postoperative posterior (∆ = - 0.01 ± 0.30) and total corneal powers (∆ = - 0.32D ± 1.08)., Conclusions: Corneal power estimation using conventional K for IOL power calculation is invalid in eyes with Fuch's dystrophy undergoing DMEK. To avoid an overestimation of corneal power and minimize the risk of a postoperative hyperopic shift, conventional K for IOL power calculation should be adjusted in eyes with Fuch's dystrophy undergoing cataract surgery combined with DMEK. The fictitious PPPA ratio and keratometer index may guide further IOL power calculation methods to achieve this.

Macular edema occurs in a wide variety of ophthalmological diseases. The diagnostics and treatment are an important part of modern ophthalmology. Due to the continuous development, artificial intelligence (AI) offers many opportunities to improve the management of macular edema. This article provides the readership with an overview of this interesting topic.

Background: Due to the increasing digitalization in ophthalmology, increasingly more documentation and diagnostic data are becoming electronically available. In order to improve patient care beyond sectoral boundaries, electronic cooperation portals were introduced in Münster and Homburg, which serve the networking with ophthalmologists in private practice., Method: This article reports on the implementation of an electronic cooperation portal at the University Eye Hospitals in Münster and Homburg in 2015 and 2016., Results: The FIDUSweb represents a data protection-compliant cooperation portal that can be easily set up. By implementing it as a web portal all terminal equipment and also electronic patient files can be networked. Particularly important functions are the transmission of medical reports in real time, appointment booking functions, the transmission of imaging data and shared treatment documentation., Conclusion: Cross-sectoral care can greatly benefit from the use of the FIDUSweb, especially in digital and multimodal ophthalmology.

Purpose: Multiple image averaging (MIA) is a new approach to improve OCT angiography (OCTA) imaging. The aim of this work was to analyze the impact of MIA on image quality and quantitative OCTA parameters., Methods: Twenty eyes from 20 healthy volunteers (55.65 ± 14.8 years) were prospectively enrolled. Imaging was performed using two commercially available OCTA devices (Canon OCT HS-100, Optovue AngioVue) using a uniform imaging protocol. Each participant had two single scans of the macula (3 × 3mm, Canon and Optovue) as well as five continuous single scan imaging procedures (3 × 3mm each) using the Canon device. Three out of five of these images with highest quality were manually chosen and then automatically processed by the Canon device using MIA. The superficial retinal plexus of the single scans and of MIA images was analyzed with regard to the device' own image quality scores (IQS), peak signal-to-noise ratio (PSNR), the size of the foveolar avascular zone (FAZ), and vessel density (VD). Image acquisition times were recorded. Parameters were compared between the devices and the different imaging protocols., Results: Average acquisition time was significantly higher for the MIA compared with the single measurements (29.09 ± 10.19 seconds (s) (MIA) vs. 5.56 ± 2.17 s (Canon single scan) vs. 20.28 ± 6.81 s (Optovue) (p < 0.001)). IQS showed no significant differences between the devices and between the recording protocols. PSNR was 12.38 ± 0.20 (Canon single scan), 13.01 ± 0.36 (Canon MIA), and 14.34 ± 0.60 (Optovue) (p < 0.001 between the groups). Mean FAZ area in Canon single scans was 0.29 ± 0.06 mm 2 , 0.27 ± 0.07 mm 2 using MIA, and 0.27 ± 0.08 mm 2 using the Optovue device. There was no significant difference between mean FAZ measurements before and after averaging (Canon single scan vs. MIA, p = 0.168). VD of the parafoveal area using MIA was significantly lower compared with both single scans (p < 0.001)., Conclusions: MIA can improve PSNR, but it also reduces imaging speed and significantly affects VD measurements. Therefore, when comparing OCTA data, the use of uniform imaging protocols is required.

Purpose: To expedite and to standardize the process of image quality assessment in optical coherence tomography angiography (OCTA) using a specialized deep learning algorithm (DLA)., Methods: Two hundred randomly chosen en-face macular OCTA images of the central 3 × 3 mm 2 superficial vascular plexus were evaluated retrospectively by an OCTA experienced reader. Images were defined either as sufficient (group 1, n = 100) or insufficient image quality (group 2, n = 100) based on Motion Artifact Score (MAS) and Segmentation Accuracy Score (SAS). Subsequently, a pre-trained multi-layer deep convolutional neural network (DCNN) was trained and validated with 160 of these en-face OCTA scans (group 1: 80; group 2: 80). Training accuracy, validation accuracy, and cross-entropy were computed. The DLA was tested in detecting 40 untrained OCTA images (group 1: 20; group 2: 20). An insufficient image quality probability score (IPS) and a sufficient image quality probability score (SPS) were calculated., Results: Training accuracy was 97%, validation accuracy 100%, and cross entropy 0.12. A total of 90% (18/20) of the OCTA images with insufficient image quality and 90% (18/20) with sufficient image quality were correctly classified by the DLA. Mean IPS was 0.88 ± 0.21, and mean SPS was 0.84 ± 0.19. Discrimination between both groups was highly significant (p < 0.001). Sensitivity of the DLA was 90.0%, specificity 90.0%, and accuracy 90.0%. Coefficients of variation were 0.96 ± 1.9% (insufficient quality) and 1.14 ± 1.6% (sufficient quality)., Conclusions: Deep learning (DL) appears to be a potential approach to automatically distinguish between sufficient and insufficient OCTA image quality. DL may contribute to establish image quality standards in this recent imaging modality.

Purpose: Real-life data provide an insight into the reality of patient care under everyday conditions. Digitization in ophthalmology has led to a structured documentation of patient data. This makes it possible to automate the retrieval of a huge amount of real-life data by developing suitable query algorithms., Methods: Using an automated query from fully electronic patient documentation, real-life therapy data were obtained in patients with intravitreal operative drug delivery (IVOM) in age-related macular degeneration (AMD) and vein occlusion (VO). Among other things, injection frequency, frequency of visits and best-corrected visual acuity over a total of 4 years of treatment were recorded. The influence of factors, such as patient age, distance to the clinic and type of insurance on the quality of care were also investigated. Treatment-naïve and pretreated patients were analyzed., Results: In the first year AMD patients received an average of 4.59 ± 2.00 injections with 10.29 ± 4.47 visits, in the second year 3.83 ± 2.23 injections with 7.72 ± 5.78 visits, in the third year 3.40 ± 2.18 injections with 7.19 ± 5.44 visits and in the fourth year 3.11 ± 2.29 injections with 6.46 ± 6.20 visits. The VO patients received an average of 4.17 ± 2.15 injections with 9.60 ± 4.88 visits in the first year, 3.31 ± 2.03 injections with 7.75 ± 4.88 visits in the second year, 2.94 ± 2.00 injections with 6.55 ± 4.77 visits in the third year and 3.03 ± 1.94 injections with 7.18 ± 5.26 visits in the fourth year. The course of the visual acuity was relatively stable over 4 years. With repect to the quality of care, a younger patient age and a closer distance to the clinic seemed to have a positive influence., Conclusion: The study results show a positive trend towards an improved quality of care in intravitreal injection therapy in patients with AMD and VO. The age of the patient and the place of residence are factors influencing the therapy.

Purpose: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm., Methods: In this study, FAF images of patients with GA, a healthy comparable group and a comparable group with other retinal diseases (ORDs) were used to train a multi-layer deep convolutional neural network (DCNN) (1) to detect GA and (2) to differentiate in GA between a diffuse-trickling pattern (dt-GA) and other GA FAF patterns (ndt-GA) in FAF images. 1. For the automated detection of GA in FAF images, two classifiers were built (GA vs. healthy/GA vs. ORD). The DCNN was trained and validated with 400 FAF images in each case (GA 200, healthy 200, or ORD 200). For the subsequent testing, the built classifiers were then tested with 60 untrained FAF images in each case (AMD 30, healthy 30, or ORD 30). Hereby, both classifiers automatically determined a GA probability score and a normal FAF probability score or an ORD probability score. 2. To automatically differentiate between dt-GA and ndt-GA, the DCNN was trained and validated with 200 FAF images (dt-GA 72; ndt-GA 138). Afterwards, the built classifier was tested with 20 untrained FAF images (dt-GA 10; ndt-GA 10) and a dt-GA probability score and an ndt-GA probability score was calculated. For both classifiers, the performance of the training and validation procedure after 500 training steps was measured by determining training accuracy, validation accuracy, and cross entropy., Results: For the GA classifiers (GA vs. healthy/GA vs. ORD), the achieved training accuracy was 99/98%, the validation accuracy 96/91%, and the cross entropy 0.062/0.100. For the dt-GA classifier, the training accuracy was 99%, the validation accuracy 77%, and the cross entropy 0.166. The mean GA probability score was 0.981 ± 0.048 (GA vs. healthy)/0.972 ± 0.439 (GA vs. ORD) in the GA image group and 0.01 ± 0.016 (healthy)/0.061 ± 0.072 (ORD) in the comparison groups (p < 0.001). The mean dt-GA probability score was 0.807 ± 0.116 in the dt-GA image group and 0.180 ± 0.100 in the ndt-GA image group (p < 0.001)., Conclusion: For the first time, this study describes the use of a deep learning-based algorithm to automatically detect and classify GA in FAF. Hereby, the created classifiers showed excellent results. With further developments, this model may be a tool to predict the individual progression risk of GA and give relevant information for future therapeutic approaches.

Purpose: To assess the prevalences of segmentation errors and motion artifacts in optical coherence tomography angiography (OCT-A) in different retinal diseases METHODS: In a retrospective analysis, multimodal retinal imaging including OCT-A was performed in one eye of 57 healthy controls (50.96 ± 22.4 years) and 149 patients (66.42 ± 14.1 years) affected by different chorioretinal diseases: early/intermediate age-related macular degeneration (AMD; n = 26), neovascular AMD (nAMD; n = 22), geographic atrophy due to AMD (GA; n = 6), glaucoma (n = 28), central serous chorioretinopathy (CSC; n = 14), epiretinal membrane (EM; n = 26), retinal vein occlusion (RVO; n = 11), and retinitis pigmentosa (RP; n = 16). Central 3 × 3 mm 2 OCT-A imaging was performed with active eye-tracking (AngioVue, Optovue). Best-corrected visual acuity (BCVA) and signal strength index (SSI) were recorded. Images were independently evaluated by two graders using the OCT-A motion artifact score (MAS; scores I-IV) as well as a newly introduced segmentation accuracy score (SAS; score I-IIB)., Results: Mean SSI was 63.67 ± 9.2 showing a negative correlation with increasing age (rSp = - 0.42, p < 0.001, n = 206). In the healthy cohort, mean MAS was 1.45 ± 0.8 and segmentation was accurate (SAS I) in all eyes. In eyes with retinal pathologies, mean MAS was 2.1 ± 0.9 (p < 0.001). Lowest MAS was observed in GA (2.67 ± 0.5) and RVO (2.45 ± 1.1). Compared to an accurate segmentation in 100% in healthy subjects, 34.2% (n = 51) of all patients showed highest segmentation quality (p < 0.001). 63.8% showed segmentation errors in more than 5% of all single b-scans in one (SAS IIA, n = 58) or at least two (SAS IIB, n = 40) segmentation boundaries. Highest percentages of inaccurate segmentation (SAS IIA or IIB) were observed in the nAMD group (90.1%). The inner plexiform layer was the segmentation boundary most prone to inaccurate segmentation in all pathologies compared to the inner limiting membrane (ILM) and retinal pigment epithelium (RPE) segmentation layer. Incorrect ILM segmentation was only seen in patients with EM., Conclusions: Prior to both qualitative and quantitative analysis, OCT-A images must be carefully reviewed as motion artifacts and segmentation errors in current OCT-A technology are frequent particularly in pathologically altered maculae.

Deep learning is increasingly becoming the focus of various imaging methods in medicine. Due to the large number of different imaging modalities, ophthalmology is particularly suitable for this field of application. This article gives a general overview on the topic of deep learning and its current applications in the field of optical coherence tomography. For the benefit of the reader it focuses on the clinical rather than the technical aspects.

Background: To determine and compare the frequency of intraocular hemorrhage in patients who underwent oral anticoagulation with apixaban or phenprocoumon., Methods: A retrospective analysis of patients under oral anticoagulant medication (apixaban or phenprocoumon) seen between January 2015 and June 2015 at the department of ophthalmology, University of Muenster Medical Center was performed. Vitreal or retinal hemorrhage in addition to clinical information including age, gender, best corrected visual acuity, concomitant diseases, concomitant medication and therapy were obtained. Bleeding frequency in both groups was compared using Fisher's exact test., Results: A total of 172 patients were included with a mean age = 74.0 ± 10.6 years, 57.0% (n = 98) male and 43.0% (n = 74) female. In the phenprocoumon group 147 patients (3.4%, n = 5) developed a retinal or vitreal hemorrhage. In the apxiban group 25 patients (36%, n = 9) developed a retinal or vitreal hemorrhage. There was a significant correlation between the group and bleeding risk (p < 0.001)., Conclusion: There was a significant correlation between medication (apixaban vs. phenprocoumon) and bleeding risk in this study population. Further studies with more patients especially in patients with a high risk of hemorhage, age-related macular degeneration (AMD) and proliferative diabetic retinopathy are needed.

Purpose: Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT)., Methods: A total of 1112 cross-section SD-OCT images of patients with exudative AMD and a healthy control group were used for this study. In the first step, an open-source multi-layer deep convolutional neural network (DCNN), which was pretrained with 1.2 million images from ImageNet, was trained and validated with 1012 cross-section SD-OCT scans (AMD: 701; healthy: 311). During this procedure training accuracy, validation accuracy and cross-entropy were computed. The open-source deep learning framework TensorFlow™ (Google Inc., Mountain View, CA, USA) was used to accelerate the deep learning process. In the last step, a created DCNN classifier, using the information of the above mentioned deep learning process, was tested in detecting 100 untrained cross-section SD-OCT images (AMD: 50; healthy: 50). Therefore, an AMD testing score was computed: 0.98 or higher was presumed for AMD., Results: After an iteration of 500 training steps, the training accuracy and validation accuracies were 100%, and the cross-entropy was 0.005. The average AMD scores were 0.997 ± 0.003 in the AMD testing group and 0.9203 ± 0.085 in the healthy comparison group. The difference between the two groups was highly significant (p < 0.001)., Conclusions: With a deep learning-based approach using TensorFlow™, it is possible to detect AMD in SD-OCT with high sensitivity and specificity. With more image data, an expansion of this classifier for other macular diseases or further details in AMD is possible, suggesting an application for this model as a support in clinical decisions. Another possible future application would involve the individual prediction of the progress and success of therapy for different diseases by automatically detecting hidden image information.

Purpose: To quantitatively compare the flow density, the retinal thickness, and the area of the foveal avascular zone (FAZ) between patients with adult-onset foveomacular vitelliform dystrophy (AOFVD) and a healthy controls., Methods: Thirteen eyes (eight patients) with AOFVD and 13 matched eyes (13 patients) without any ocular pathology were included in this study. A 6 × 6 mm optical coherence tomography angiography (OCTA) scan was performed for every included eye. The flow density (superficial retinal vascular layer, deep retinal vascular layer and choriocapillary layer), retinal thickness and FAZ (superficial retinal vascular layer and deep retinal vascular layer) were subsequently analyzed., Results: The mean flow density was decreased in the AOFVD patients in all measured vascular layers. The difference from the control group was statistically significant in the parafoveal sector of the deep retinal vascular layer (P = 0.02), and a clear trend was found in the superficial retinal vascular layer (P = 0.05). Both groups had comparable FAZs in the superficial and deep retinal vascular layers. The retinal thickness values were higher in the fovea (P = 0.840) and lower in the parafoveal sectors (P = 0.125). The difference was significant in the superior parafoveal sector (P = 0.034)., Conclusions: Flow densities as measured by OCTA are decreased in the superficial retinal vascular layer and the deep retinal vascular layer in patients with AOFVD. These findings could be helpful for diagnosing and understanding the pathogenesis of this disease.

Objective: To evaluate the impact of eye-tracking (ET) technology on optical coherence tomography angiography (OCT-A) image quality and manifestation of motion artifacts in patients with age-related macular degeneration (AMD)., Methods: In a prospective trial, multimodal retinal imaging including OCT-A was performed in 30 patients (78.97 ± 9.7 years) affected by different stages of AMD. Central 3 × 3 mm 2 OCT-A imaging was performed four times consecutively in each patient, twice with active, and twice with inactive ET. Parameters for image evaluation were signal strength index (SSI), variability of foveal vessel density (VD), acquisition time, presence of motion artifacts caused by eye movement (blink lines, displacement) and by software correction of eye movement (quilting, stretch artifacts, vessel doubling). Images were evaluated by two independent readers with subsequent senior reader arbitration for presence of artifacts, and an OCT-A motion artifact score (MAS) was calculated., Results: Eight patients had early and eight patients had intermediate stages of AMD. Four patients had an atrophic late stage and ten patients an exudative stage of the disease. SSI was 53.55 with inactive and 57.18 with active ET (p = 0.0005). Coefficients of variability of VD between the first and second measurement were 8.9% with inactive and 5.7% with active ET. Mean image acquisition time was 15.97 s (active ET: 22.88 s, p < 0.001). Presence of motion artifacts was significantly higher with inactive ET (mean MAS 3.27 vs. 1.93; p < 0.0001). MAS correlated with AMD disease stage [p = 0.0031 (inactive ET) and p < 0.0001 (active ET)] and with SSI (p = 0.0072 and p = 0.0006)., Conclusions: In patients with AMD, active ET technology offers an improved image quality in OCT-A imaging regarding presence of motion artifacts at the expense of higher acquisition time.

Purpose: To evaluate the effect of a preexisting glaucoma on the early postoperative outcome of a descemet membrane endothelial keratoplasty (DMEK)., Methods: All patients who underwent DMEK surgery at the Department of Ophthalmology of the University of Muenster with a follow-up of at least 3 months (90d) were included in this study. The best corrected distance visual acuity (BCDVA), the intraocular pressure (IOD), the rate of re-keratoplasty and the rebubbling rate were inter alia recorded. The results of patients with (group 1) and without a preexisting glaucoma (group 2) were compared., Results: 74 eyes of 59 patients with a mean follow-up of 152 ± 70 days were included. 65 eyes were in group 1 and 9 eyes in group 2. The BCDVA significantly improved in both groups after surgery (p < 0.03). The Re-keratoplasty rate (p = 0.172), the number of rebubblings per patient (p = 0.571) and the rebubbling rate (p = 0.939) were not significantly different in patients without glaucoma compared to patients with a preexisting glaucoma., Conclusions: In the early stage outcome of DMEK no significant impact of a preexisting glaucoma was found.