Steve Lenier

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This article was reviewed by Jeffrey Tapley, MD

In patients with endothelial dysfunction, performing Descemet membrane endothelial keratoplasty (DMEK) rapidly deturgesces an edematous cornea. In some cases that deturgescence reveals posterior corneal steepening similar to keratoconus, but the authors note recent studies have shown there is no genetic overlap between Fuchs’ dystrophy and keratoconus.

A retrospective review was performed to describe changes in corneal thickness, and identify the incidence of posterior corneal changes characteristic of keratoconus, following DMEK surgery.

The study was a retrospective chart review that included 50 eyes that received cataract surgery and 50 eyes that received DMEK for Fuchs’ Dystrophy. Scheimpflug images (done with an Oculus Pentacam) for each eye were reviewed. For the DMEK group, follow up Pentacam images from 6 months and 1 year were used. The central corneal thickness (CCT) of the preoperative cataract surgery eyes served as normal controls and was compared to the CCT of the postoperative DMEK eyes.

The 6 month and 1 year postoperative Pentacam images in the DMEK group were also reviewed for posterior elevation changes and localized steepening, characteristics that resemble keratoconus. Mean age, gender, preoperative CCT, and postoperative peripheral corneal thickness (PCT) were recorded. Statistical significance comparing CCT, PCT, and CCT:PCT was determined using the Student’s T-test.

Normal CCT from the 50 control eyes was 543 µm ± 35 µm. The 6-month postoperative CCT in the DMEK group was 534 µm±35 µm (P=0.23) and the 1-year CCT was 536 µm ± 35 µm (P=0.33).

19 out of 50 DMEK eyes (38%) had posterior steepening resembling keratoconus at the 6 month visit.

A subgroup analysis of the preoperative CCT from those 19 eyes vs 31 eyes without keratoconus-like changes was not statistically different (611 µm ± 51 µm vs 615 µm ± 56 µm, P = 0.80).

All 19 of those eyes had the same posterior corneal changes at the 1 year visit.

The authors concluded that CCT after DMEK was not statistically different compared to normal corneas at 6 months and 1 year postoperative. Localized posterior corneal steepening characteristic of keratoconus was seen in 38% of DMEK corneas at 6 months following surgery. The localized steepening persists out to 1 year postoperative, suggesting that there may be structural loss in the stroma. The steepening was not a function of preoperative CCT or postoperative PCT, and the authors believe it is likely a consequence of stromal keratocyte apoptosis due to chronic edema.

Limitations of the study include its retrospective nature and the small sample size, and it is noted that future studies should be performed focusing on identifying predictive factors for these corneal changes.

This article is based on Tapley’s presentation at the ARVO 2021 virtual annual meeting. Tapley has no financial disclosures.

Articles

A retrospective review was performed to describe changes in corneal thickness and to identify the incidence of posterior corneal changes characteristic of keratoconus, following DMEK surgery. Here’s what investigators found.

Considering keratoconus-like changes after DMEK in Fuchs' dystrophy


This article was reviewed by Sanjay Patel, MD

The authors performed this study in order to develop a severity scale for tomographic edema in Fuchs’ endothelial corneal dystrophy (FECD) based on Scheimpflug tomography maps, and to evaluate intra- and inter-observer variation for assessing severity.

They explained that Scheimpflug imaging can be used to detect tomographic patterns consistent with the presence of edema in FECD. 

These patterns are found in the posterior elevation and pachymetry maps, and are repeatable, and can be used to predict the prognosis of FECD. 

The authors of this study developed a severity scale for FECD based on these maps, and evaluated intra- and inter-observer variation for assessing severity.

This was a cross-sectional study in which eyes with a range of severity of FECD were examined by two cornea specialists and underwent Scheimpflug imaging with a Pentacam (Oculus).

Scheimpflug tomography pachymetry and posterior elevation maps were assessed for features of subclinical corneal edema, and an ordinal scale was developed to assess the severity of FECD based on the presence and extent of the tomographic features. 

The scale ranges from normal (grade 1) to severe (grade 6) edema, based on loss of parallel isopachs, displacement of the thinnest point of the cornea, and posterior surface depression within the central 4 mm of the cornea. 

Subtle findings suspicious for early tomographic edema were defined as grade 2. 

Progressive severity of edema was based on the depth of posterior surface depression (mild, moderate, advanced, and severe were grades 3-6, respectively).

Observers first standardized their grading with a training set of images. The severity of tomographic edema was then graded as 1-6 from

masked and randomized images, which were presented to both cornea specialists without associated clinical information.

Intra-observer (one observer evaluating the same images on two occasions separated by two weeks) and inter-observer agreement were assessed by using the kappa statistic (κ) with 95% confidence interval (CI), and McNemar’s test was used to assess for significant differences.



In this study, exact agreement between repeated assessments by one observer occurred for 97% (277/286) of eyes (κ=0.96, CI 0.93 to 0.99, p=0.88). All disagreements were by 1 grade only.

Exact agreement between assessments by both observers occurred for 85% (244/286) of eyes (κ=0.81, CI 0.76 to 0.86, p=0.12). 

Disagreements were by 1 grade for 40 eyes and by 2 grades for 2 eyes, and mainly occurred because one observer assigned increased severity relative to the other when determining normal tomography from those with suspicious findings of early edema.



The authors showed that assessing the severity of tomographic edema in FECD has excellent intra- and inter-observer agreement based on the proposed severity scale. 

They say the lower inter-observer agreement was more a result of lack of sufficient standardization between observers rather than limitations of the imaging technology, especially for findings suspicious of early edema.

They point out further investigation is needed to determine if this severity assessment can detect tomographic progression and how it relates to clinical findings


This article is based on Patel’s presentation at the ARVO 2021 virtual annual meeting. Patel is a consultant to GlaxoSmithKline, Senju Pharmaceuticals, Santen Inc., and Emmecell. None are relevant to this topic.

Investigators sought to develop a severity scale for tomographic edema in FECD based on Scheimpflug tomography maps, and to evaluate intra- and inter-observer variation for assessing severity.

Assessing the severity of Fuchs’ endothelial corneal dystrophy by using Scheimpflug tomography


This article was reviewed by Brandon Baksh, BA, and Anat Galor, MD, MSPH

Chronic pain is a common and often debilitating condition. Chronic pain syndromes can be localized to the head and face, and involve the trigeminal system, for example in migraine and trigeminal neuralgia, explained Brandon Baksh, BA.

He continued, saying chronic pain syndromes can also be widespread, such as in fibromyalgia, or localized to areas outside the head and face, such as back pain, and that nerve abnormalities have been linked to chronic pain syndromes.

“As eyecare providers,” he said, “we can assess this link between nerve abnormalities and chronic pain.” 

He pointed out that corneal sensitivity can be measured in the office.


Corneal sensitivity is altered in several eye conditions. However, its relationship with chronic pain conditions outside of the eye is not clear.

To learn more about how chronic pain conditions outside of the eye impact corneal sensitivity, the authors investigated whether individuals with chronic trigeminal pain conditions have differences in corneal sensitivity compared with individuals with chronic pain conditions not localized to the trigeminal system.

Their hypothesis was that patients with chronic trigeminal pain have differences in corneal mechanical detection threshold compared to those who have chronic pain conditions that are not localized to the trigeminal system.

A cross sectional study was performed of South Florida veterans with chronic pain conditions (>3 months duration) seen at an eye clinic. 

The main independent variables were pain diagnoses and locations, as recorded by the participants through a survey. 

The main dependent variable was corneal mechanical detection threshold, which was assessed using a modified Belmonte aesthesiometer.

To assess confounding variables, data was collected on demographics, dry eye symptoms and signs, mental health indices, systemic co-morbidities, and quality of life indices.

Individuals were split into two groups based on their pain diagnosis:

chronic pain conditions that involved the trigeminal system

chronic pain conditions that did not localize to the trigeminal system


Corneal mechanical threshold was assessed with a modified Belmonte aesthesiometer:

Pulses of air at room temperature were used, on the right eye.

Started at 10mL/min, and waited 15 seconds between pulses

Increased by 10mL/min, until the patient could feel the pulse

This was repeated a 2nd time and the mean was used

If the difference between the 2 trials was > 20, a 3rd reading was done

Baksh noted that a lower detection threshold using the Belmonte aesthesiometer meant a higher corneal sensitivity.

Continuous variables were assessed via the independent t-test. Correlations were assessed via Pearson r coefficient, and categorical variables were assessed via Chi-square test. 

All reported p-values are two-tailed and p<0.05 was considered statistically significant.



This study included 568 individuals with one or more chronic pain conditions. The mean age was 61 +/- 10.5 years. 89% were male, 44% self-identified as White, and 25% self-identified as Hispanic. 

The mean corneal mechanical detection threshold was 81 +/- 38 mL/min, with a range of 10-240 mL/min.

The authors found that the Trigeminal pain group (n=122) had a lower mean corneal mechanical detection threshold (i.e. higher sensitivity) (73+/-31) compared to the Other pain group (n=446, mean threshold 84+/-40). This was statistically significant.

The authors also assessed continuous variables. Variables that came out significantly correlated with lower corneal mechanical detection threshold (higher sensitivity) included a high depression score, a high PTSD score, worse dry eye symptoms, worse eye pain, as well as worse quality of life, lower age, lower cholesterol and lower triglycerides.

Lower corneal mechanical detection threshold (higher sensitivity) may be associated with pain phenotypes such as the presence of chronic pain involving the trigeminal system, 5 or more pain conditions, and the presence of arthritis.

Beyond pain phenotypes, additional factors that may play a role include a higher depression score, Black race, dyslipidemia, and CPAP use.


This article is based on Baksh’s presentation at the ARVO 2021 virtual annual meeting. Baksh has no financial disclosures.

A cross sectional study investigated how chronic pain conditions outside of the eye impact corneal sensitivity. 

Corneal mechanical sensitivity in individuals with chronic pain

In traditional machine learning, a programmer tells the machine the features of whatever it is supposed to identify. In deep learning, the features are picked out by the neural network, without human intervention. 

Instead, the program is given a large sample of appropriately labeled images, and the program is able to learn to recognize patterns by itself, without pre-existing rules set by the programmer. 

In recent years, convolutional neural networks (CNNs) have been developed that provide higher accuracy in image detection tasks using multiple layers to progressively extract higher level features. 

These CNNs can be used to automatically grade retina images of patients with diabetic retinopathy, and also have been used in glaucoma, but there is a “paucity of research” in the field of cornea.

Keratoconus is a progressive disease that leads to the thinning of the cornea. If caught early, disease progression can be halted, using corneal collagen crosslinking.

Despite treatment, approximately 10% of patients continue to progress, eventually requiring corneal transplantation.

The goal of this study was to implement and test a trained deep learning algorithm using pre-operative corneal topography scans in order to 1) classify between keratoconus and normal corneas, 2) stage the disease according to Amsler-Krumeich scale, and 3) predict whether a patient will be likely to benefit from crosslinking treatment.

Oculus Pentacam images were processed and standardized for comparison. The algorithm used is an 11-layer convolutional neural network programmed using TensorFlow software with the assistance of a data scientist (

Visualized model of the algorithm being used for image comparison.

A method called cross-validation was used to test the method. The data is split into two parts, a training set and a validation set. 

The training set uses 80% of the image set to train the model, and the validation set uses 20% of the images to evaluate the model’s performance, including images the network has not seen before.

Final accuracies were reported after 50 epochs (one cycle through the full training dataset) for all three stages of the study.

In total, 2450 scans were included (1215 keratoconus and 1235 controls) for stage 1, 985 keratoconus scans were classified according to the Amsler-Krumeich scale for stage 2, and 138 keratoconus scans (69 progressed and 69 stabilized) were labeled for stage 3.

In stage 1, differentiating eyes with keratoconus from normal eyes, a very high validation accuracy rate (99.3%) was achieved. For stage 2, grading keratoconus, the validation accuracy was 73.5%. 

One reason to account for the lower accuracy is the fact that the grading system used does not incorporate manifest refraction data. Implementing a marker system, the authors were able to improve the accuracy 14%, to 87.8%. 

Because there were more images used in stage 1 than in stage 2, the authors re-did the testing of stage 1 with fewer images, and achieved a validation accuracy of 83.6%, which is closer to that found in stage 2.

For stage 3, assessing the accuracy of the CNN in predicting whether a patient with keratoconus will stabilize following crosslinking, or continue to progress, the images were split into groups of stabilized vs progressed. 

The program was unable to learn any useful parameters that would improve its accuracy in categorizing the scans, and only achieved a validation accuracy of 53.6%.

The sample size may have limited this study.Another limitation is that with deep learning algorithms it’s not possible to visualize what parameters the algorithm is using for classification. 

However researchers have now developed class activation maps to highlight regions to better elucidate the inner workings of the deep learning black box. 

Last, the authors noted the results of this detection is limited to Pentacams, and without more images it can’t be generalized to other topographers such as the OrbScan.


The authors were able to build and test a deep learning model that was able to diagnose keratoconus with an accuracy of 99.3%, and stage the disease at 87.8%. 

They said that even though it was not able to predict disease progression, the applications of convolutional neural networks are encouraging and can be utilized to solve practical clinical problems and serve as an adjunct tool in clinical decision making in the near future.


This article is based on Dr. Liu‘s presentation at the ARVO 2021 virtual annual meeting. Dr. Liu has no financial disclosures.

A study sought to implement and test a trained deep learning algorithm using pre-operative corneal topography scans in order to classify between keratoconus and normal corneas, stage the disease, and predict whether a patient will be likely to benefit from CXL treatment.

Steve Lenier

Articles

Assessing the severity of Fuchs’ endothelial corneal dystrophy by using Scheimpflug tomography

Corneal mechanical sensitivity in individuals with chronic pain

Deep learning for detection of keratoconus and prediction of CXL efficacy

Considering keratoconus-like changes after DMEK in Fuchs' dystrophy