Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence–Derived Morphometric Parameters From Specular Microscopy Images Article Swipe
Angélica M. Prada
,
Fernando Quintero
,
Kevin Mendoza
,
Virgilio Galvis
,
Alejandro Tello
,
Lenny A. Romero
,
Andrés G. Marrugo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1097/ico.0000000000003460
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1097/ico.0000000000003460
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/mm 2 vs. 2216 ± 509 cells/mm 2 , 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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1097/ico.0000000000003460
- OA Status
- hybrid
- Cited By
- 10
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391676858
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391676858Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1097/ico.0000000000003460Digital Object Identifier
- Title
-
Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence–Derived Morphometric Parameters From Specular Microscopy ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-09Full publication date if available
- Authors
-
Angélica M. Prada, Fernando Quintero, Kevin Mendoza, Virgilio Galvis, Alejandro Tello, Lenny A. Romero, Andrés G. MarrugoList of authors in order
- Landing page
-
https://doi.org/10.1097/ico.0000000000003460Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1097/ico.0000000000003460Direct OA link when available
- Concepts
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Ophthalmology, Grading (engineering), Microscopy, Grading scale, Medicine, Pathology, Biology, Surgery, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.potential | 241 |
| abstract_inverted_index.processed | 64 |
| abstract_inverted_index.recruited | 31 |
| abstract_inverted_index.slit-lamp | 41 |
| abstract_inverted_index.strongest | 169 |
| abstract_inverted_index.underwent | 37 |
| abstract_inverted_index.variation | 86 |
| abstract_inverted_index.artificial | 12, 136 |
| abstract_inverted_index.assessment | 245 |
| abstract_inverted_index.compelling | 221 |
| abstract_inverted_index.evaluation | 211 |
| abstract_inverted_index.marginally | 192 |
| abstract_inverted_index.microscope | 140 |
| abstract_inverted_index.microscopy | 25 |
| abstract_inverted_index.monitoring | 260 |
| abstract_inverted_index.parameters | 15 |
| abstract_inverted_index.peripheral | 180 |
| abstract_inverted_index.recruited, | 113 |
| abstract_inverted_index.therapies. | 263 |
| abstract_inverted_index.assessments | 46 |
| abstract_inverted_index.coefficient | 84 |
| abstract_inverted_index.correlation | 170, 195 |
| abstract_inverted_index.endothelial | 20, 45, 78 |
| abstract_inverted_index.estimation, | 75 |
| abstract_inverted_index.large-scale | 238 |
| abstract_inverted_index.microscopy. | 49, 214 |
| abstract_inverted_index.parameters. | 106 |
| abstract_inverted_index.significant | 133, 193 |
| abstract_inverted_index.Conclusions: | 200 |
| abstract_inverted_index.applications | 242 |
| abstract_inverted_index.evaluations, | 39 |
| abstract_inverted_index.examinations | 42 |
| abstract_inverted_index.irreversible | 247 |
| abstract_inverted_index.measurements | 144 |
| abstract_inverted_index.morphometric | 14, 73, 222 |
| abstract_inverted_index.photographs. | 129 |
| abstract_inverted_index.segmentation | 71 |
| abstract_inverted_index.convolutional | 67 |
| abstract_inverted_index.discrepancies | 134 |
| abstract_inverted_index.hexagonality. | 90 |
| abstract_inverted_index.mixed-effects | 92 |
| abstract_inverted_index.relationships | 98 |
| abstract_inverted_index.characterizing | 17 |
| abstract_inverted_index.classification | 103 |
| abstract_inverted_index.ophthalmologic | 38 |
| abstract_inverted_index.classification. | 60 |
| abstract_inverted_index.cross-sectional | 29 |
| abstract_inverted_index.retroillumination | 128 |
| abstract_inverted_index.phacoemulsification | 252 |
| abstract_inverted_index.intelligence–based | 137 |
| abstract_inverted_index.intelligence–derived | 13 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.95845705 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |