Optimizing intraocular lens power calculation using adjusted conventional keratometry for cataract surgery combined with Descemet membrane endothelial keratoplasty.

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.

Assessing the validity of corneal power estimation using conventional keratometry for intraocular lens power calculation in eyes with Fuch's dystrophy undergoing Descemet membrane endothelial keratoplasty.

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.

CILIORETINAL ARTERIES INFLUENCE OPTIC NERVE HEAD, PERIPAPILLARY, AND MACULAR VESSEL DENSITIES IN HEALTHY EYES: An Optical Coherence Tomography Angiography Study.

BACKGROUND/PURPOSE: To analyze the influence of a cilioretinal artery (CRA) on macular and peripapillary vessel density in healthy eyes as measured using optical coherence tomography angiography. METHODS: A total of 83 eyes of 83 patients were included in this study. Optical coherence tomography angiography was performed using the RTVue XR Avanti with AngioVue (Optovue Inc). The macula was imaged with a 3 × 3-mm scan, whereas for the optic nerve head a 4.5 × 4.5-mm scan was taken. Optical coherence tomography angiography images of the optic nerve head were screened for the presence of a CRA. RESULTS: In 31 eyes, a CRA was detected (37.3%). The vessel density in eyes with a CRA was significantly lower within the optic nerve head (P = 0.005) but higher in the peripapillary capillary network (P < 0.001) and (whole en face) macular superficial capillary plexus (P = 0.025), when compared with eyes with no CRA. CONCLUSION: Our findings reveal that in eyes with a CRA, the vessel density in the peripapillary and macular superficial capillary plexus is increased, whereas the optic nerve head perfusion (as indicated by vessel density in the inside disk region) is decreased. This has to be considered when analyzing quantitative optical coherence tomography angiography parameters in scientific and clinical applications.

[Chances of Artificial Intelligence and Big Data for the Diagnosis and Treatment of Age-related Macular Degeneration]. / Chancen von künstlicher Intelligenz und Big Data für die Diagnostik und Behandlung der altersabhängigen Makuladegeneration.

Age-related macular degeneration (AMD) is the leading cause of blindness in the western world. Intravitreal injection of anti-vascular endothelial growth factor (anti-VEGF) is an effective therapy of the neovascular form of this condition. Multimodal imaging and standardised electronic patient documentation have helped to improve the diagnosis and management of AMD patients recent years. With the advent of artificial intelligence and big data, there are many opportunities for the future. This article is intended to give an overview of possible applications.

[Expression of Motion Artifacts in OCT-Angiography Imaging in Healthy Subjects Using Two Different Devices]. / Vergleich der Bildqualität zweier unterschiedlicher OCT-Angiografie-Systeme mit Fokus auf Bewegungsartefakten bei gesunden Probanden.

BACKGROUND: To compare the expression of motion artifacts in optical coherence tomography angiography (OCT-A) in healthy subjects using two different devices. METHODS: In this study, 25 eyes of 25 healthy volunteers with no history of any ocular disease or ocular surgery were included. OCT-A imaging was performed using the RTVue XR Avanti (Optovue Inc., Fremont, California, USA) and the Spectralis OCT-A (Heidelberg Engineering, Heidelberg, Deutschland). The macula was imaged twice in each proband with active eye tracking (ET) using a 3 × 3 mm2 or a 10 × 10° scan, respectively. The expression of motion artifact was analyzed by two independent readers in the superficial OCT-angiogram using the Motion Artifact Score (MAS). RESULTS: The signal strength index (SSI) was 73.0 ± 7.8 (Optovue) and 39.6 ± 3.6 (Heidelberg), which is equivalent to 73.0% (Optovue SSImax = 100 = 100%) and 79.2% (SSImax = 50 = 100%) of the maximum quality score. Both devices showed a very good image quality (mean MAS Optovue: 1.32 ± 0.551, mean MAS Heidelberg: 1.7 ± 0.789, p = 0.006). Of all measurements, quilting/banding was found in 20% of Optovue patients (10/50) and 6% of Heidelberg patients (3/50). Stretching was found in 4% of Optovue patients (2/50) and in 6% of Heidelberg patients (3/50). Vessel doubling was only seen in one Optovue angiogram (2%) as well as a displacement (2%). Blink lines only existed in three Heidelberg angiograms (6%). CONCLUSION: Despite different software and hardware approaches, both devices were able to take high-quality images with a very low prevalence of motion artifacts. Nevertheless, these artifacts still also occur in healthy subjects with good fixation. With regards to MAS, there was a high agreement between the two readers. However, the analysis of artifacts remains complex and requires experience as well as a precise assessment in evaluating OCT-A images.

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.

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.

Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

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.

Quantitative changes in flow density in patients with adult-onset foveomacular vitelliform dystrophy: an OCT angiography study.

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.

Descemet membrane endothelial keratoplasty (DMEK) early stage graft failure in eyes with preexisting glaucoma.

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.

SHORT-TERM EFFECTS OF EXERCISE ON OPTIC NERVE AND MACULAR PERFUSION MEASURED BY OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY.

PURPOSE: To evaluate the effects of exercise on optic nerve and macular perfusion using optical coherence tomography angiography. METHODS: Thirteen eyes of 13 healthy volunteers were examined using a high-speed and high-resolution spectral-domain optical coherence tomography XR Avanti with a split-spectrum amplitude-decorrelation angiography algorithm. Blood pressure, heart rate, the mean area of the foveal avascular zone , and flow density on the optic nerve head and macula, before and after exercise were measured and analyzed. RESULTS: Mean patient age was 27.3 ± 3.5 years. Heart rate, systolic and diastolic blood pressure increased significantly after exercise (P < 0.001). The mean area of the foveal avascular zone did not change significantly after exercise (before: 0.27 ± 0.07 mm; after: 0.26 ± 0.07 mm; P = 0.10). The peripapillary and the parafoveal flow density decreased significantly after exercise (peripapillary: before: 65.1 ± 2.1; after: 62.3 ± 3.0; P < 0.001 and parafoveal: before: 56.7 ± 1.3; after: 55.6 ± 1.5; P = 0.007). CONCLUSION: Increased physical activity induced significant changes in optic nerve and macular perfusion, which were measured using split-spectrum amplitude-decorrelation angiography optical coherence tomography angiography. In studies that aim to evaluate optic nerve and macular perfusion using optical coherence tomography angiography, it should be strongly recommended that patients rest before imaging is performed and that data concerning systemic circulation including blood pressure and pulse is included within the evaluation.

Comparison of Choriocapillaris Flow Measurements between Two Optical Coherence Tomography Angiography Devices.

PURPOSE: To evaluate choriocapillaris (CC) perfusion in healthy subjects using 2 different optical coherence tomography angiography (OCT-A) devices. PROCEDURES: Macular OCT-A imaging (36 eyes of 36 subjects) was performed using Optovue AngioVue and Zeiss AngioPlex devices. CC decorrelation signal index was assessed, and CC data were analyzed regarding intra-device variability, inter-device correlation, age, signal strength, and fields of view. RESULTS: The intra-device variability of CC measurements in the 3 × 3 mm2 field was 5.3 and 2.6% (Angiovue and Angioplex, coefficients of variation; 6 × 6 mm2: 8.0 and 2.8%, respectively). Mean CC decorrelation signal index in 3 × 3 mm2 was 104.3 ± 6.7 (Angiovue) and 81.3 ± 9.2 (Angioplex) (6 × 6 mm2: 95.6 ± 8.1, 81.1 ± 6.5) with high correlation between both devices (3 × 3 mm2: p = 0.0053; 6 × 6 mm2: p = 0.0139). CC decorrelation signal index in 3 × 3 mm2 was significantly higher in subjects aged ≤58 years compared to subjects aged ≥59 years (Angiovue: 107.3 ± 3.6, 101.3 ± 7.7, p = 0.0156; Angioplex: 84.6 ± 7.6, 78.0 ± 9.5, p = 0.0371). Signal strength was 64.6 ± 8.9 (Angiovue) and 9.5 ± 0.8 (Angioplex). CONCLUSION: Both devices showed low intra-device variability and a high inter-device correlation. CC decorrelation signal index was negatively correlated with advancing age.

Influence of Different Tibial Fixation Techniques on Initial Stability in Single-Stage Anterior Cruciate Ligament Revision With Confluent Tibial Tunnels: A Biomechanical Laboratory Study.

PURPOSE: To kinematically and biomechanically compare 4 different types of tibial tunnel management in single-stage anterior cruciate ligament (ACL) revision reconstruction with the control: primary ACL reconstruction using a robotic-based knee testing setup. METHODS: Porcine knees and flexor tendons were used. One hundred specimens were randomly assigned to 5 testing groups: (1) open tibial tunnel, (2) bone plug technique, (3) biodegradable interference screw, (4) dilatation technique, and (5) primary ACL reconstruction. A robotic/universal force-moment sensor testing system was used to simulate the KT-1000 (MEDmetric, San Diego, CA) and pivot-shift tests. Cyclic loading and load-to-failure testing were performed. RESULTS: Anterior tibial translation increased significantly with all of the techniques compared with the intact ACL (P < .05). In the simulated KT-1000 test, groups 2 and 3 achieved results equal to those of primary ACL reconstruction (P > .05). The open tunnel and dilated tunnel techniques showed significantly greater anterior tibial translation (P < .05). The results of the simulated pivot-shift test were in accordance with those of the KT-1000 test. No significant differences could be observed regarding stiffness or maximum load to failure. However, elongation was significantly lower in the primary ACL reconstruction group compared with groups 1 and 3 (P = .02 and P = .03, respectively). CONCLUSIONS: Filling an incomplete and incorrect tibial tunnel with a press-fit bone plug or a biodegradable interference screw in a standardized laboratory situation provided initial biomechanical properties and knee stability comparable with those of primary ACL reconstruction. In contrast, the dilatation technique or leaving the malplaced tunnel open did not restore knee kinematics adequately in this model. Backup extracortical fixation should be considered because the load to failure depends on the extracortical fixation when an undersized interference screw is used for aperture fixation. CLINICAL RELEVANCE: Our biomechanical results could help orthopaedic surgeons to optimize the results of primary ACL revision with incomplete, incorrect tunnel placement.

Discriminating Healthy Optic Discs and Visible Optic Disc Drusen on Fundus Autofluorescence and Color Fundus Photography Using Deep Learning-A Pilot Study.

The aim of this study was to use deep learning based on a deep convolutional neural network (DCNN) for automated image classification of healthy optic discs (OD) and visible optic disc drusen (ODD) on fundus autofluorescence (FAF) and color fundus photography (CFP). In this study, a total of 400 FAF and CFP images of patients with ODD and healthy controls were used. A pre-trained multi-layer Deep Convolutional Neural Network (DCNN) was trained and validated independently on FAF and CFP images. Training and validation accuracy and cross-entropy were recorded. Both generated DCNN classifiers were tested with 40 FAF and CFP images (20 ODD and 20 controls). After the repetition of 1000 training cycles, the training accuracy was 100%, the validation accuracy was 92% (CFP) and 96% (FAF), respectively. The cross-entropy was 0.04 (CFP) and 0.15 (FAF). The sensitivity, specificity, and accuracy of the DCNN for classification of FAF images was 100%. For the DCNN used to identify ODD on color fundus photographs, sensitivity was 85%, specificity 100%, and accuracy 92.5%. Differentiation between healthy controls and ODD on CFP and FAF images was possible with high specificity and sensitivity using a deep learning approach.

Submacular Hemorrhages Show No Significant Seasonal Variations in a European Cohort.

The aim of the article is to investigate the seasonality of acute submacular hemorrhages (SMHs) in a European population and analyze the influence of the seasons, arterial hypertension, and intake of anticoagulatory/antiplatelet (AC/AP) medication on hemorrhage size. This retrospective, monocentric study included 164 eyes of 164 patients treated for acute SMH at the University Hospital Münster, Germany, between 1 January 2016 and 31 December 2021. Data on the day of occurrence, hemorrhage size, and general patient characteristics were recorded. "Test for cyclic trends in incidence data" and the Chi-Square Test were applied to investigate seasonal variations in SMH incidence. Fisher's exact test was used to investigate the influence of the seasons, arterial hypertension, and intake of AC/AP medication on hemorrhage size. A statistical analysis did not reveal significant seasonal variations in the occurrence of SMHs (p = 0.81). While the seasons and the presence of systemic arterial hypertension did not exert a significant influence, the intake of AC/AP medication significantly affected the size of SMH (p = 0.03). In this European cohort, no significant seasonal variations of SMHs were observed. However, in patients with risk factors, such as neovascular age-related macular degeneration (nAMD), the chance of an increase in hemorrhage size should be considered when initiating AC/AP therapy.

Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study.

PURPOSE: To evaluate a deep learning-based method to automatically detect graft detachment (GD) after Descemet membrane endothelial keratoplasty (DMEK) in anterior segment optical coherence tomography (AS-OCT). METHODS: In this study, a total of 1172 AS-OCT images (609: attached graft; 563: detached graft) were used to train and test a deep convolutional neural network to automatically detect GD after DMEK surgery in AS-OCT images. GD was defined as a not completely attached graft. After training with 1072 of these images (559: attached graft; 513: detached graft), the created classifier was tested with the remaining 100 AS-OCT scans (50: attached graft; 50 detached: graft). Hereby, a probability score for GD (GD score) was determined for each of the tested OCT images. RESULTS: The mean GD score was 0.88 ± 0.2 in the GD group and 0.08 ± 0.13 in the group with an attached graft. The differences between both groups were highly significant (P < 0.001). The sensitivity of the classifier was 98%, the specificity 94%, and the accuracy 96%. The coefficient of variation was 3.28 ± 6.90% for the GD group and 2.82 ± 3.81% for the graft attachment group. CONCLUSIONS: With the presented deep learning-based classifier, reliable automated detection of GD after DMEK is possible. Further work is needed to incorporate information about the size and position of GD and to develop a standardized approach regarding when rebubbling may be needed.

Integrating x4T-EDC into an Image-Portal to Establish an Ophthalmic Reading Center.

Reading centers provide centralized high-quality diagnostics in ophthalmic clinical trials. Since ophthalmic images are captured in electronic format at peripheral clinics, an integrated workflow for image transfer and creation of structured reports is needed, including quality assurance. The image portal and the study database are separate components. We assessed whether this integration is feasible with trial-related IT standards and built a prototype system as a proof-of-concept. CDISC ODM and OAuth authentication were used to integrate the image portal with x4T-EDC, facilitating automatic data transfer and single sign-on.