Diagnostic Performance of 3-Dimensional Thickness of the Endothelium-Descemet Complex in Fuchs' Endothelial Cell Corneal Dystrophy

3D Mapping of the DMEK Detachment Area from OCT Scans of the Anterior Segment of the Eye - Clinical Applications

Graft detachment is the most common complication after Descemet membrane endothelial keratoplasty (DMEK). To assess the amount of graft detachment, precision is limited when using slit-lamp biomicroscopy. Detachment of DMEK grafts can be assessed automatically on anterior segment optical coherence tomography (AS OCT) images and allows visualization of the area and volume of detachment using 3D maps. This article provides an overview of its applications such as accurately assessing the course of natural graft attachment, identification of potential risk factors for detachment and evaluation of the long-term effect of graft detachment. The 3D map of DMEK detachment may support researchers and clinicians in precise quantification of the area and volume of graft detachment even in large data sets, and the intuitive, fast and reliable evaluation.

Applications of Imaging Technologies in Fuchs Endothelial Corneal Dystrophy: A Narrative Literature Review

Fuchs endothelial corneal dystrophy (FECD) is a complex genetic disorder characterized by the slow and progressive degeneration of corneal endothelial cells. Thus, it may result in corneal endothelial decompensation and irreversible corneal edema. Moreover, FECD is associated with alterations in all corneal layers, such as thickening of the Descemet membrane, stromal scarring, subepithelial fibrosis, and the formation of epithelial bullae. Hence, anterior segment imaging devices that enable precise measurement of functional and anatomical changes in the cornea are essential for the management of FECD. In this review, the authors will introduce studies on the application of various imaging modalities, such as anterior segment optical coherence tomography, Scheimpflug corneal tomography, specular microscopy, in vitro confocal microscopy, and retroillumination photography, in the diagnosis and monitoring of FECD and discuss the results of these studies. The application of novel technologies, including image processing technology and artificial intelligence, that are expected to further enhance the accuracy, precision, and speed of the imaging technologies will also be discussed.

Differences in Guttae Ultramorphology in Relation to Visual Function in Fuchs Endothelial Corneal Dystrophy

PURPOSE

The purpose of this study was to investigate the differences in guttae ultramorphology and their relation to visual function in eyes with Fuchs endothelial corneal dystrophy (FECD).

METHODS

Thirty FECD eyes without ocular comorbidities were included. Visual functional parameters (best-corrected visual acuity with high-contrast and low-contrast letters and contrast sensitivity/LogCS) and corneal morphology measured with Scheimpflug tomography (Pentacam) were assessed. The surgically removed Descemet membranes were examined by light and transmission electron microscopy.

RESULTS

Preoperative mean best-corrected visual acuity (logarithm of the minimum angle of resolution) was 0.52 ± 0.18, LogCS 0.96 ± 0.21 and central corneal thickness 640 ± 55 μm. All eyes had signs of subclinical corneal edema in Scheimpflug tomography; clinically visible corneal edema was present in 40% of eyes. Histological findings included a posterior fibrillar zone (PFZ) in 10 specimens (33%) and abnormal collagen depositions in Descemet membranes in 14 specimens (47%). Guttae buried within the PFZ were present only in eyes with clinically visible edema (n = 4, 13%). There was no difference in visual function results and tomography parameters between eyes with and without PFZ or between protruding guttae and guttae embedded in a PFZ, respectively.

CONCLUSIONS

Guttae morphology and density were not correlated with visual functional parameters. Guttae buried in a PFZ occurred only in eyes with clinically manifest edema, and thereby, they are an ultramorphological sign for advanced FECD. Subclinical edema was present in all eyes and might be more relevant for quality of vision than guttae ultramorphology.

Systematic Review of the Diagnostic Criteria and Severity Classification for Fuchs Endothelial Corneal Dystrophy

PURPOSE

There are no defined diagnostic criteria and severity classification for Fuchs endothelial corneal dystrophy (FECD), which are required for objective standardized assessments. Therefore, we performed a systematic literature review of the current diagnosis and severity classification of FECD.

METHODS

We searched the Ovid MEDLINE and Web of Science databases for studies published until January 13, 2021. We excluded review articles, conference abstracts, editorials, case reports with <5 patients, and letters.

RESULTS

Among 468 articles identified, we excluded 173 and 165 articles in the first and second screenings, respectively. Among the 130 included articles, 61 (47%) and 99 (76%) mentioned the diagnostic criteria for FECD and described its severity classification, respectively. Regarding diagnosis, slitlamp microscope alone was the most frequently used device in 31 (51%) of 61 articles. Regarding diagnostic findings, corneal guttae alone was the most common parameter [adopted in 23 articles (38%)]. Regarding severity classification, slitlamp microscopes were used in 88 articles (89%). The original or modified Krachmer grading scale was used in 77 articles (78%), followed by Adami's classification in six (6%). Specular microscopes or Scheimpflug tomography were used in four articles (4%) and anterior segment optical coherence tomography in one (1%).

CONCLUSIONS

FECD is globally diagnosed by the corneal guttae using slitlamp examination, and its severity is predominantly determined by the original or modified Krachmer grading scale. Objective severity grading using Scheimpflug or anterior segment optical coherence tomography can be applied in the future innovative therapies such as cell injection therapy or novel small molecules.

Descemet Membrane Endothelial Keratoplasty (DMEK) Reduces the Corneal Epithelial Thickness in Fuchs' Patients

Fuchs' endothelial corneal dystrophy (FECD) is the occurrence of corneal edema due to endothelial cell dystrophy. Descemet membrane endothelial keratoplasty (DMEK) is considered to be the gold standard of treatment. The aim of this study was to investigate the changes in the corneal epithelial thickness of FECD patients before and after DMEK and to compare these results with a healthy control cohort. In this retrospective analysis, 38 eyes of patients with FECD that were treated with DMEK and 35 healthy control eyes received anterior segment optical coherence tomography (OCT; Optovue, XR-Avanti, Fremont, CA, USA). The corneal epithelial thicknesses in different locations were analyzed and compared between the preoperative, postoperative, and control cohorts. The median follow-up time was 9 months. There was a significant degression of the mean epithelial thickness after DMEK in the central, paracentral, and mid-peripheral zones (p < 0.01) of the cornea. The total corneal thickness and stromal thickness decreased significantly as well. No significant differences were observed between the postoperative and control cohorts. In conclusion, the FECD patients had an increased epithelial thickness compared to the healthy controls, which decreased significantly after DMEK and reached thickness levels comparable to those of healthy control eyes. This study emphasized the importance of distinguishing between the corneal layers in anterior segment pathologies and surgical procedures. Moreover, it accentuated the fact that the structural alterations in FECD extend beyond the corneal stroma.

Comparison of Autonomous AS-OCT Deep Learning Algorithm and Clinical Dry Eye Tests in Diagnosis of Dry Eye Disease

Objective

To evaluate a deep learning-based method to autonomously detect dry eye disease (DED) in anterior segment optical coherence tomography (AS-OCT) images compared to common clinical dry eye tests.

Methods

In this study, 27,180 AS-OCT images were prospectively collected from 151 eyes of 91 patients. Images were used to train and test the deep learning model. Masked cornea specialist ophthalmologist diagnoses were used as the gold standard. Clinical dry eye tests were performed on patients in the DED group to compare the results of the model. The dry eye tests performed were tear break-up time (TBUT), Schirmer's test, corneal staining, conjunctival staining, and Ocular Surface Disease Index (OSDI).

Results

Our deep learning model achieved an accuracy of 84.62%, sensitivity of 86.36%, and specificity of 82.35% in the diagnosis of DED. The positive likelihood ratio was 4.89, and the negative likelihood ratio was 0.17. The mean DED probability score was 0.81 ± 0.23 in the DED group and 0.20 ± 0.27 in the healthy group (P < 0.01). The deep learning model accuracy in the diagnosis of DED was significantly better than that of corneal staining, conjunctival staining, and Schirmer's test (P < 0.05). There was no significant difference between the deep learning diagnostic accuracy and that of the OSDI and TBUT.

Conclusion

Based on preliminary results, reliable autonomous diagnosis of DED with our deep learning model was achieved, when compared with standard dry eye clinical tests that correlated significantly more or similarly to diagnoses made by cornea specialist ophthalmologists.

Separate Detection of Stromal and Epithelial Corneal Edema on Optical Coherence Tomography Using a Deep Learning Pipeline and Transfer Learning

The accurate detection of corneal edema has become a topic of growing interest with the generalization of endothelial keratoplasty. Despite recent advances in deep learning for corneal edema detection, the problem of minimal edema remains challenging. Using transfer learning and a limited training set of 11 images, we built a model to segment the corneal epithelium, which is part of a three-model pipeline to detect corneal edema. A second and a third model are used to detect edema on the stroma alone and on the epithelium. A validation set of 233 images from 30 patients consisting of three groups (Normal, Minimal Edema and important Edema) was used to compare the results of our new pipeline to our previous model. The mean edema fraction (EF), defined as the number of pixels detected as edema divided by the total number of pixels of the cornea, was calculated for each image. With our previous model, the mean EF was not statistically different between the Normal and Minimal Edema groups (p = 0.24). With the current pipeline, the mean EF was higher in the Minimal Edema group compared to the Normal group (p < 0.01). The described pipeline constitutes an adjustable framework for the detection of corneal edema based on optical coherence tomography and yields better performances in cases of minimal or localized edema.

Prediction of corneal graft rejection using central endothelium/Descemet's membrane complex thickness in high-risk corneal transplants

To determine whether measurements of Endothelium/Descemet complex thickness (En/DMT) are of predictive value for corneal graft rejection after high-risk corneal transplantation, we conducted this prospective, single-center, observational case series including sixty eyes (60 patients) at high risk for corneal graft rejection (GR) because of previous immunologic graft failure or having at least two quadrants of stromal vascularization. Patients underwent corneal transplant. At 1st, 3rd, 6th, 9th, and 12th postoperative month, HD-OCT imaging of the cornea was performed, and the corneal status was determined clinically at each visit by a masked cornea specialist. Custom-built segmentation tomography algorithm was used to measure the central En/DMT. Relationships between baseline factors and En/DMT were explored. Time dependent covariate Cox survival regression was used to assess the effect of post-operative En/DMT changes during follow up. A longitudinal repeated measures model was used to assess the relationship between En/DMT and graft status. Outcome measures included graft rejection, central Endothelium/Descemet's complex thickness, and central corneal thickness (CCT). In patients with GR (35%), the central En/DMT increased significantly 5.3 months (95% CI: 2, 11) prior to the clinical diagnosis of GR, while it remained stable in patients without GR. During the 1-year follow up, the rejected grafts have higher mean pre-rejection En/DMTs (p = 0.01), compared to CCTs (p = 0.7). For En/DMT ≥ 18 µm cut-off (at any pre-rejection visit), the Cox proportional hazard ratio was 6.89 (95% CI: 2.03, 23.4; p = 0.002), and it increased to 9.91 (95% CI: 3.32, 29.6; p < 0.001) with a ≥ 19 µm cut-off. In high-risk corneal transplants, the increase in En/DMT allowed predicting rejection prior to the clinical diagnosis.

Multidisease Deep Learning Neural Network for the Diagnosis of Corneal Diseases

PURPOSE

To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images.

STUDY DESIGN

Development of a deep learning neural network diagnosis algorithm.

METHODS

A total of 158,220 AS-OCT images from 879 eyes of 478 subjects were used to develop and validate a classification deep network. After a quality check, the network was trained and validated using 134,460 images. We tested the network using a test set of consecutive patients involving 23,760 AS-OCT images of 132 eyes of 69 patients. The area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and F1 score and 95% confidence intervals (CIs) were computed.

RESULTS

The MDDN achieved eye-level AUROCs >0.99 (95% CI: 0.90, 1.0), AUPRCs > 0.96 (95% CI: 0.90, 1.0), and F1 scores > 0.90 (95% CI: 0.81, 1.0) for DES, FED, and KCN, respectively.

CONCLUSIONS

MDDN is a novel diagnostic tool for corneal diseases that can be used to automatically diagnose KCN, FED, and DES using only AS-OCT images.

Short-Term Anterior Segment Changes After Nd-YAG Laser Posterior Capsulotomy in Pseudophakic Eyes with Fuchs' Endothelial Dystrophy

Purpose

To describe the changes in the central corneal thickness (CCT), endothelial cell count (ECC), intraocular lens (IOL) position, and refractive error 1 and 3 months after Nd-YAG laser posterior capsulotomy (YAG-PC) for posterior capsular opacification (PCO) in pseudophakic eyes with Fuchs' endothelial cell dystrophy (FECD).

Design

Prospective case-control.

Participants

Fifty pseudophakic eyes of 50 patients with visually significant PCO (25 with healthy corneas, and 25 with FECD grade 1 and 2).

Methods

FECD was clinically graded, and only patients without clinically evident corneal edema were included in the study (grade 1 and 2). All patients received YAG-PC and were followed up after 1 and 3 months. Best-corrected visual acuity, refractive errors, ACD, CCT, and ECC were assessed and compared between the pre-laser values and that of the 2 follow-up visits.

Results

In both groups, BCVA showed significant improvement (P<0.05). Compared to healthy controls, a significant hyperopic shift was observed in the FECD eyes at the 2 follow-up visits (P=0.027, and 0.019). A significant backward movement of the IOL was observed in the FECD eyes in the 2 follow-up visits (P=0.043, and 0.02). There was a significant correlation between the hyperopic shift in the FECD eyes and the ACD deepening during the 1st month (R= -0.6, P=0.001) and 3rd month (R= -0.4, P=0.04). Significant drop in the ECC was noted in both groups but without significant increase in the CCT.

Conclusion

Compared to controls, a hyperopic shift and backward movement of the IOL were observed in the FECD eyes after YAG-PC. No significant corneal thickening was found in both groups despite the significant drop in the ECC. Future studies are required to confirm our results and monitor the long-term changes after YAG-PC in FECD.

Corneal Analysis with Swept Source Optical Coherence Tomography in Patients with Coexisting Cataract and Fuchs Endothelial Corneal Dystrophy

This study focused on defining the characteristic features of keratometry and pachymetry elevation maps based on swept source optical coherence tomography (SS OCT) in Fuchs endothelial corneal dystrophy (FECD) eyes with a coexisting cataract. 70 eyes of 35 patients diagnosed with FECD and a coexisting cataract and 70 control eyes were included in this prospective, controlled, observational, cross-sectional study. Features characteristic of intermediately affected eyes included an increased corneal thinnest thickness (CTT) (p = 0.01), 3 and 6 mm asymmetry (p < 0.0001), higher order Fourier indices (p < 0.05 and p ≤ 0.0001, respectively), chord µ, and a posterior Ectasia Screening Index (pESI) (p < 0.01). The lack of agreement between the anterior and posterior elevation map and a significant area of negative values in the posterior map were detected. In advanced FECD eyes, our study additionally revealed decreased posterior keratometry steep (Ks), keratometry flat (Kf), keratometry average (AvgK), eccentricity (Ecc), an increased corneal apex thickness (CAT), and decreased 3 and 6 mm posterior spherical indices (p < 0.0001 for all of the above). Characteristic features of subclinical FECD, independent of the corneal thickness, can be detected by SS OCT and should be considered during the preoperative assessment of patients with a coexisting cataract.

Unique corneal tomography features of allergic eye disease identified by OCT imaging and artificial intelligence

The purpose of this study was to assess unique corneal tomographic parameters of allergic eye disease (AED) using optical coherence tomography (OCT) and artificial intelligence (AI). A total of 57 eyes diagnosed with AED were included. The curvature and aberrations of the air-epithelium (A-E) and epithelium-Bowman's layer (E-B) interfaces were calculated. Random forest AI models were built combing this data with the parameters of healthy, forme fruste keratoconus (FFKC) and KC eyes. The AI models were cross-validated with 3-fold random sampling. Each model was limited to 10 trees. The AI model incorporating both A-E and E-B parameters provided the best classification of AED eyes (area under the curve = 0.958, sensitivity = 80.7%, specificity = 98.5%, precision = 88.2%). Further, the E-B interface parameters provided the highest information gain in the AI model. A few AED eyes (n = 9) had tomography parameters similar to FFKC and KC eyes and may be at risk of progression to KC.

Automated diagnosis and staging of Fuchs' endothelial cell corneal dystrophy using deep learning

Background

To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs’ endothelial corneal dystrophy (FECD) without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography (HD-OCT).

Methods

In this observational case-control study, 104 eyes (53 FECD eyes and 51 healthy controls) received HD-OCT imaging (Envisu R2210, Bioptigen, Buffalo Grove, IL, USA) using a 6 mm radial scan pattern centered on the corneal vertex. FECD was clinically categorized into early (without corneal edema) and late-stage (with corneal edema). A total of 18,720 anterior segment optical coherence tomography (AS-OCT) images (9180 healthy; 5400 early-stage FECD; 4140 late-stage FECD) of 104 eyes (81 patients) were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical edema. Using 5-fold cross-validation on the dataset containing 11,340 OCT images (63 eyes), the network was trained with 80% of these images (3420 healthy; 3060 early-stage FECD; 2700 late-stage FECD), then tested with 20% (720 healthy; 720 early-stage FECD; 720 late-stage FECD). Thereafter, a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-OCT scans of 41 eyes (5040 healthy; 1620 early-stage FECD 720 late-stage FECD). Visualization of learned features was done, and area under curve (AUC), specificity, and sensitivity of the prediction outputs for healthy, early and late-stage FECD were computed.

Results

The final model achieved an AUC of 0.997 ± 0.005 with 91% sensitivity and 97% specificity in detecting early-FECD; an AUC of 0.974 ± 0.005 with a specificity of 92% and a sensitivity up to 100% in detecting late-stage FECD; and an AUC of 0.998 ± 0.001 with a specificity 98% and a sensitivity of 99% in discriminating healthy corneas from all FECD.

Conclusion

Deep learning algorithm is an accurate autonomous novel diagnostic tool of FECD with very high sensitivity and specificity that can be used to grade FECD severity with high accuracy.