Literature DB >> 31678558

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

Taher K Eleiwa1, Jane C Cook2, Amr S Elsawy3, Vatookarn Roongpoovapatr2, Vincent Volante2, Sonia Yoo2, Mohamed Abou Shousha4.   

Abstract

PURPOSE: To evaluate the performance of 3-dimensional (3D) endothelium/Descemet membrane complex thickness (En/DMT) maps vs total corneal thickness (TCT) maps in the diagnosis of active corneal graft rejection.
DESIGN: Cross-sectional study.
METHODS: Eighty-one eyes (32 clear grafts and 17 with active rejection, along with 32 age-matched control eyes) were imaged using high-definition optical coherence tomography (HD-OCT), and a custom-built segmentation algorithm was used to generate 3D color-coded maps of TCT and En/DMT of the central 6-mm cornea. Regional En/DMT and TCT were analyzed and compared between the studied groups. Receiver operating characteristic curves were used to determine the accuracy of En/DMT and TCT maps in differentiating between studied groups. Main outcome measures were regional En/DMT and TCT.
RESULTS: Both regional TCT and En/DMT were significantly greater in actively rejecting grafts compared to both healthy corneas and clear grafts (P < .001). Using 3D thickness maps, central, paracentral, and peripheral En/DMT achieved 100% sensitivity and 100% specificity in diagnosing actively rejecting grafts (optimal cut-off value [OCV] of 19 μm, 24 μm, and 26 μm, respectively), vs only 82% sensitivity and 96% specificity for central TCT, OCV of 587 μm. Moreover, central, paracentral, and peripheral En/DMT correlated significantly with graft rejection severity (r = 0.972, r = 0.729, and r = 0.823, respectively; P < .001).
CONCLUSION: 3D En/DMT maps can diagnose active corneal graft rejection with excellent accuracy, sensitivity, and specificity. Future longitudinal studies are required to evaluate the predictive and prognostic role of 3D En/DMT maps in corneal graft rejection.
Copyright © 2019 Elsevier Inc. All rights reserved.

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Year:  2019        PMID: 31678558      PMCID: PMC7002262          DOI: 10.1016/j.ajo.2019.10.022

Source DB:  PubMed          Journal:  Am J Ophthalmol        ISSN: 0002-9394            Impact factor:   5.258


  5 in total

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

Authors:  Ce Shi; Mengyi Wang; Tiantian Zhu; Ying Zhang; Yufeng Ye; Jun Jiang; Sisi Chen; Fan Lu; Meixiao Shen
Journal:  Eye Vis (Lond)       Date:  2020-09-10

2.  Correlation of Clinical Fibrillar Layer Detection and Corneal Thickness in Advanced Fuchs Endothelial Corneal Dystrophy.

Authors:  Orlando Özer; Mert Mestanoglu; Antonia Howaldt; Thomas Clahsen; Petra Schiller; Sebastian Siebelmann; Niklas Reinking; Claus Cursiefen; Björn Bachmann; Mario Matthaei
Journal:  J Clin Med       Date:  2022-05-17       Impact factor: 4.964

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

Authors:  Taher Eleiwa; Amr Elsawy; Eyüp Özcan; Mohamed Abou Shousha
Journal:  Eye Vis (Lond)       Date:  2020-09-01

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

Authors:  Collin Chase; Amr Elsawy; Taher Eleiwa; Eyup Ozcan; Mohamed Tolba; Mohamed Abou Shousha
Journal:  Clin Ophthalmol       Date:  2021-10-21

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

Authors:  Taher Eleiwa; Amr Elsawy; Eyup Ozcan; Collin Chase; William Feuer; Sonia H Yoo; Victor L Perez; Mohamed F Abou Shousha
Journal:  Sci Rep       Date:  2021-07-15       Impact factor: 4.379

  5 in total

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