Diagnostic Performance of Three-Dimensional Endothelium/Descemet Membrane Complex Thickness Maps in Active Corneal Graft Rejection

Eleiwa, Taher; Cook, Jane C.; Elsawy, Amr; Roongpoovapatr, Vatookarn; Volante, Vincent; Yoo, Sonia H.; Shousha, Mohamed Abou

doi:10.1016/j.ajo.2019.10.022

Cited by 12 publications

(12 citation statements)

References 35 publications

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“…This may result from the fact that current commercial anterior-segment OCT does not have enough bandwidth to detect the earliest changes with KC occurring at the level of Bowman’s layer, even with our UHR-OCT system. Future technical developments, such as new UHR-OCTs at an axial resolution of 1.5 μm level [ 37 , 38 ] combined with 3D Bowman’s layer topography [ 39 , 40 ] and deep learning automated corneal segmentation techniques [ 41 ], can help scientists and clinicians detect the true earliest change of the Bowman’s layer in subclinical KC patients.…”

Section: Discussionmentioning

confidence: 99%

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

et al. 2020

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Purpose To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography (UHR-OCT) imaging data. Methods A total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups: normal (50 eyes), with keratoconus (38 eyes) or with subclinical keratoconus (33 eyes). All eyes were imaged with a Scheimpflug camera and UHR-OCT. Corneal morphological features were extracted from the imaging data. A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes. Fisher’s score was used to rank the differentiable power of each feature. The receiver operating characteristic (ROC) curves were calculated to obtain the area under the ROC curves (AUCs). Results The developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus (AUC = 0.93). The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes. Conclusion The automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes. The epithelial features extracted from the OCT images were the most valuable in the discrimination process. This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.

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Section: Discussionmentioning

confidence: 99%

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

et al. 2020

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“…Thus, it is important to consider coexisting subtle corneal pathologies prior to interpreting the tomographic maps [ 4 , 5 ]. Currently, with the advent of anterior segment optical coherence tomography (AS-OCT), it has become credible to perform non-contact in vivo imaging to evaluate the corneal microstructure of FECD cases at a quasi-histologic level [ 20 – 23 ]. Nevertheless, there is still a barrier in deploying this technology for clinical practice due to the lack of automated reliable and accurate analysis of OCT scans [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Anterior segment HD-OCT (Envisu R2210, Bioptigen, Buffalo Grove, IL, USA) with 6 mm radial cuts centered on the corneal vertex was performed at 1st, 3rd, 6th, 9th, and 12th month after surgery. The characteristics of this device were described in our previous work 13 , 16 . To confirm optimal centration, each candidate was asked to fix at a target to visualize a saturation artifact in all images.…”

Section: Methodsmentioning

confidence: 99%

“…Central 3D-En/DM complex and central corneal thickness were the diagnostic indices generated from the segmented HD-OCT images. We previously reported the 2 interfaces of the En/DM as the two most posterior hyperreflective bands of the cornea on HD-OCT images 13 , 21 .…”

Section: Methodsmentioning

confidence: 99%

“…Recently, we developed and validated a segmental tomography algorithm to generate 3D thickness maps of different corneal layers using AS-OCT corneal scans to enhance the diagnosis of different corneal diseases 12 – 15 . Using this tool, the three-dimensional thickness of the endothelial/Descemet membrane complex has shown excellent sensitivity and specificity in diagnosing corneal graft rejection that strongly correlated with the rejection severity 13 . Hereafter, we hypothesized that En/DMT could potentially reveal early changes that herald corneal graft rejection.…”

Section: Introductionmentioning

confidence: 99%

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

Eleiwa

Elsawy

Ozcan

et al. 2021

Sci Rep

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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.

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Diagnostic Performance of Three-Dimensional Endothelium/Descemet Membrane Complex Thickness Maps in Active Corneal Graft Rejection

Cited by 12 publications

References 35 publications

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

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

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

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