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Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images
dc.contributor.author | Prada, Angelica M. | |
dc.contributor.author | Quintero, Fernando | |
dc.contributor.author | Mendoza, Kevin | |
dc.contributor.author | Galvis, Virgilio | |
dc.contributor.author | Tello, Alejandro | |
dc.contributor.author | Romero, Lenny A | |
dc.contributor.author | Marrugo, Andres G. | |
dc.date.accessioned | 2024-08-12T13:21:34Z | |
dc.date.available | 2024-08-12T13:21:34Z | |
dc.date.issued | 2024-09-09 | |
dc.date.submitted | 2024-08-12 | |
dc.identifier.citation | Prada, A. M., Quintero, F., Mendoza, K., Galvis, V., Tello, A., Romero, L. A., & Marrugo, A. G. (2024). Assessing Fuchs corneal endothelial dystrophy using Artificial Intelligence–Derived morphometric parameters from specular microscopy images. Cornea. https://doi.org/10.1097/ico.0000000000003460 | spa |
dc.identifier.uri | https://hdl.handle.net/20.500.12585/12706 | |
dc.description.abstract | Purpose: The aim of this study was to evaluate the efficacy of artificial intelligence–derived morphometric parameters in characterizing Fuchs corneal endothelial dystrophy (FECD) from specular microscopy images. Methods: This cross-sectional study recruited patients diagnosed with FECD, who underwent ophthalmologic evaluations, including slit-lamp examinations and corneal endothelial assessments using specular microscopy. The modified Krachmer grading scale was used for clinical FECD classification. The images were processed using a convolutional neural network for segmentation and morphometric parameter estimation, including effective endothelial cell density, guttae area ratio, coefficient of variation of size, and hexagonality. A mixed-effects model was used to assess relationships between the FECD clinical classification and measured parameters. Results: Of 52 patients (104 eyes) recruited, 76 eyes were analyzed because of the exclusion of 26 eyes for poor quality retroillumination photographs. The study revealed significant discrepancies between artificial intelligence–based and built-in microscope software cell density measurements (1322 ± 489 cells/mm2 vs. 2216 ± 509 cells/mm2, P < 0.001). In the central region, guttae area ratio showed the strongest correlation with modified Krachmer grades (0.60, P < 0.001). In peripheral areas, only guttae area ratio in the inferior region exhibited a marginally significant positive correlation (0.29, P < 0.05). Conclusions: This study confirms the utility of CNNs for precise FECD evaluation through specular microscopy. Guttae area ratio emerges as a compelling morphometric parameter aligning closely with modified Krachmer clinical grading. These findings set the stage for future large-scale studies, with potential applications in the assessment of irreversible corneal edema risk after phacoemulsification in FECD patients, as well as in monitoring novel FECD therapies. | spa |
dc.format.extent | 8 páginas | |
dc.format.medium | ||
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | Cornea | spa |
dc.title | Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images | spa |
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datacite.rights | http://purl.org/coar/access_right/c_abf2 | spa |
oaire.version | http://purl.org/coar/version/c_b1a7d7d4d402bcce | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.hasversion | info:eu-repo/semantics/draft | spa |
dc.identifier.doi | 10.1097/ICO.0000000000003460 | |
dc.subject.keywords | Fuchs dystrophy | spa |
dc.subject.keywords | Specular microscopy | spa |
dc.subject.keywords | Endothelial cell density | spa |
dc.subject.keywords | Artificial intelligence, | spa |
dc.subject.keywords | Deep learning | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.cc | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.identifier.instname | Universidad Tecnológica de Bolívar | spa |
dc.identifier.reponame | Repositorio Universidad Tecnológica de Bolívar | spa |
dc.publisher.place | Cartagena de Indias | spa |
dc.subject.armarc | LEMB | |
dc.type.spa | http://purl.org/coar/resource_type/c_6501 | spa |
dc.audience | Investigadores | spa |
oaire.resourcetype | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.