Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images
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 underwe...
- Autores:
-
Prada, Angelica M.
Quintero, Fernando
Mendoza, Kevin
Galvis, Virgilio
Tello, Alejandro
Romero, Lenny A
Marrugo, Andres G.
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12706
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12706
- Palabra clave:
- Fuchs dystrophy
Specular microscopy
Endothelial cell density
Artificial intelligence,
Deep learning
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv |
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images |
title |
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images |
spellingShingle |
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images Fuchs dystrophy Specular microscopy Endothelial cell density Artificial intelligence, Deep learning LEMB |
title_short |
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images |
title_full |
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images |
title_fullStr |
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images |
title_full_unstemmed |
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images |
title_sort |
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images |
dc.creator.fl_str_mv |
Prada, Angelica M. Quintero, Fernando Mendoza, Kevin Galvis, Virgilio Tello, Alejandro Romero, Lenny A Marrugo, Andres G. |
dc.contributor.author.none.fl_str_mv |
Prada, Angelica M. Quintero, Fernando Mendoza, Kevin Galvis, Virgilio Tello, Alejandro Romero, Lenny A Marrugo, Andres G. |
dc.subject.keywords.spa.fl_str_mv |
Fuchs dystrophy Specular microscopy Endothelial cell density Artificial intelligence, Deep learning |
topic |
Fuchs dystrophy Specular microscopy Endothelial cell density Artificial intelligence, Deep learning LEMB |
dc.subject.armarc.none.fl_str_mv |
LEMB |
description |
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. |
publishDate |
2024 |
dc.date.accessioned.none.fl_str_mv |
2024-08-12T13:21:34Z |
dc.date.available.none.fl_str_mv |
2024-08-12T13:21:34Z |
dc.date.issued.none.fl_str_mv |
2024-09-09 |
dc.date.submitted.none.fl_str_mv |
2024-08-12 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/draft |
dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
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draft |
dc.identifier.citation.spa.fl_str_mv |
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 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12706 |
dc.identifier.doi.none.fl_str_mv |
10.1097/ICO.0000000000003460 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
identifier_str_mv |
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 10.1097/ICO.0000000000003460 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
url |
https://hdl.handle.net/20.500.12585/12706 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
dc.format.extent.none.fl_str_mv |
8 páginas |
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Pdf |
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application/pdf |
dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
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Cornea |
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Universidad Tecnológica de Bolívar |
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Prada, Angelica M.4626d69e-31ff-4ace-ad29-3dd7f23dd07cQuintero, Fernandof07ea69c-0c88-4255-be83-d0aa76f66768Mendoza, Kevin135a7756-119e-4c7c-ae0b-d7111a8a3a61Galvis, Virgilio85e1c5d8-b4a4-4bed-828d-267cd8ca4b5bTello, Alejandrob88c245a-e5d9-4feb-a8e5-fc2a6555415dRomero, Lenny A4e34aa8a-f981-4e1d-ae32-d45acb6abcf9Marrugo, Andres G.3d6cd388-d48f-4669-934f-49ca4179f5422024-08-12T13:21:34Z2024-08-12T13:21:34Z2024-09-092024-08-12Prada, 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.0000000000003460https://hdl.handle.net/20.500.12585/1270610.1097/ICO.0000000000003460Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarPurpose: 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.8 páginasPdfapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2CorneaAssessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Imagesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Fuchs dystrophySpecular microscopyEndothelial cell densityArtificial intelligence,Deep learningLEMBCartagena de IndiasInvestigadoresMatthaei M, Hribek A, Clahsen T, et al. Fuchs endothelial corneal dystrophy: clinical, genetic, pathophysiologic, and therapeutic aspects. Annu Rev Vis Sci. 2019;5:151–175.Ali M, Cho K, Srikumaran D. Fuchs dystrophy and cataract: diagnosis, evaluation and treatment. Ophthalmol Ther. 2023;12:691–704.Margo CE, Espana EM. Corneal endothelial dystrophy. In: Margo CE, Cheng JY, Espana EM, et al., eds. Ophthalmic Pathology: The Evolution of Current Concepts. Cambridge, MA: Academic Press, 2023:147–151.Galvis V, Tello A, Laiton AN, et al. Indications and techniques of corneal transplantation in a referral center in Colombia, South America (2012–2016). Int Ophthalmol. 2019;39:1723–1733.Zhang J, Patel DV. The pathophysiology of Fuchs' endothelial dystrophy—a review of molecular and cellular insights. Exp Eye Res. 2015;130:97–105.Rodrigues MM, Krachmer JH, Hackett J, et al. . Fuchs' corneal dystrophy: a clinicopathologic study of the variation in corneal edema. Ophthalmology. 1986;93:789–796.Hribek A, Clahsen T, Horstmann J, et al. . Fibrillar layer as a marker for areas of pronounced corneal endothelial cell loss in advanced Fuchs endothelial corneal dystrophy. Am J Ophthalmol. 2021;222:292–301Schrems-Hoesl L, Schrems W, Cruzat A, et al. . Cellular and subbasal nerve alterations in early stage Fuchs' endothelial corneal dystrophy: an in vivo confocal microscopy study. Eye. 2013;27:42–49.Patel SV, Hodge DO, Treichel EJ, et al. . Visual function in pseudophakic eyes with Fuchs' endothelial corneal dystrophy. Am J Ophthalmol. 2022;239:98–107Mishima S. Clinical investigations on the corneal endothelium: XXXVIII Edward Jackson Memorial Lecture. Am J Ophthalmol. 1982;93:1–29.Cornea Donor Study Investigator Group, Gal RL, Dontchev M, et al. . The effect of donor age on corneal transplantation outcome: results of the cornea donor study. Ophthalmology. 2008;115:620–626.e6Hoffer KJ. Corneal decomposition after corneal endothelium cell count. Am J Ophthalmol. 1979;87:252–253.Kocaba V, Katikireddy KR, Gipson I, et al. . Association of the gutta-induced microenvironment with corneal endothelial cell behavior and demise in Fuchs endothelial corneal dystrophy. JAMA Ophthalmol. 2018;136:886–892.Krachmer JH, Purcell JJ, Young CW, et al. . Corneal endothelial dystrophy: a study of 64 families. Arch Ophthalmol. 1978;96:2036–2039.Louttit MD, Kopplin LJ, Igo RP, et al. . A multicenter study to map genes for Fuchs endothelial corneal dystrophy: baseline characteristics and heritability. Cornea. 2012;31:26–35.Repp DJ, Hodge DO, Baratz KH, et al. . Fuchs' endothelial corneal dystrophy: subjective grading versus objective grading based on the central-to-peripheral thickness ratio. Ophthalmology. 2013;120:687–694.Mingo-Botín D, Arnalich-Montiel F, Couceiro de Juan A, et al. . Repeatability and intersession reproducibility of pentacam corneal thickness maps in Fuchs dystrophy and endothelial keratoplasty. Cornea. 2018;37:987–992.Patel SV, Hodge DO, Treichel EJ, et al. . Predicting the prognosis of Fuchs endothelial corneal dystrophy by using Scheimpflug tomography. Ophthalmology. 2020;127:315–323Sun SY, Wacker K, Baratz KH, et al. . Determining subclinical edema in Fuchs endothelial corneal dystrophy: revised classification using Scheimpflug tomography for preoperative assessment. Ophthalmology. 2019;126:195–204Yasukura Y, Oie Y, Kawasaki R, et al. . New severity grading system for Fuchs endothelial corneal dystrophy using anterior segment optical coherence tomography. Acta Ophthalmol. 2021;99:e914–e921.Aggarwal S, Cavalcanti BM, Regali L, et al. . In vivo confocal microscopy shows alterations in nerve density and dendritiform cell density in Fuchs' endothelial corneal dystrophy. Am J Ophthalmol. 2018;196:136–144.Ong Tone S, Bruha MJ, Böhm M, et al. . Regional variability in corneal endothelial cell density between guttae and non-guttae areas in Fuchs endothelial corneal dystrophy. Can J Ophthalmol. 2019;54:570–576Li Z, Wang L, Wu X, et al. . Artificial intelligence in ophthalmology: the path to the real-world clinic. Cell Rep Med. 2023;4:101095.Sierra JS, Pineda J, Rueda D, et al. . Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps. Biomed Opt Express. 2023;14:335–351McLaren JW, Bachman LA, Kane KM, et al. . Objective assessment of the corneal endothelium in Fuchs' endothelial dystrophy. Invest Ophthalmol Vis Sci. 2014;55:1184–1190Fujimoto H, Maeda N, Soma T, et al. . Quantitative regional differences in corneal endothelial abnormalities in the central and peripheral zones in Fuchs' endothelial corneal dystrophy. Invest Ophthalmol Vis Sci. 2014;55:5090–5098Eghrari AO, Garrett BS, Mumtaz AA, et al. . Retroillumination photography analysis enhances clinical definition of severe Fuchs corneal dystrophy. Cornea. 2015;34:1623–1626.Eghrari AO, Mumtaz AA, Garrett B, et al. . Automated retroillumination photography analysis for objective assessment of Fuchs corneal dystrophy. Cornea. 2017;36:44–47.Hamlett A, Ryan L, Serrano-Trespalacios P, et al. . Mixed models for assessing correlation in the presence of replication. J Air Waste Manag Assoc. 2003;53:442–450.Ying G, Maguire MG, Glynn R, et al. . Tutorial on biostatistics: statistical analysis for correlated binary eye data. Ophthalmic Epidemiol. 2018;25:1–12.Syed ZA, Tran JA, Jurkunas UV. Peripheral endothelial cell count is a predictor of disease severity in advanced Fuchs' endothelial corneal dystrophy. Cornea. 2017;36:1166–1171.Sierra J, Pineda J, Volpe G, et al. . Code for corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps. GitHub; 2022. Available at: 10.5281/zenodo.7378507.Hemaya M, Hemaya M, Habeeb A. Evaluating keratoplasty for Fuchs' endothelial corneal dystrophy: a literature review. Cureus. 2023;15:e33639.Li Z, Duan H, Jia Y, et al. . Long-term corneal recovery by simultaneous delivery of hPSC-derived corneal endothelial precursors and nicotinamide. J Clin Invest. 2022;132:e146658.Yuan A, Pineda R. Regenerative medicine in Fuchs' endothelial corneal dystrophy. 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