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Recent advances in corneal specular microscopy image analysis through artificial intelligence

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dc.contributor.authorMarrugo Hernández, Andrés Guillermo
dc.contributor.authorFernando Quintero
dc.contributor.authorTello, Alejandro
dc.contributor.authorPrada, Angélica M.
dc.contributor.authorGalvis, Virgilio
dc.contributor.authorRomero Pérez, Lenny Alexandra
dc.contributor.researchgroupGrupo de Investigación Física Aplicada y Procesamiento de Imágenes y Señales- FAPIS
dc.contributor.seedbedsSemillero de Investigación en Visión Artificial
dc.date.accessioned2026-05-11T15:25:33Z
dc.date.issued2026-03-31
dc.descriptionContiene ilustraciones, gráficos
dc.description.abstractAlthough conventional automated analysis of corneal specular microscopy images has historically been limited by reproducibility challenges in the presence of corneal guttae, recent advances in artificial intelligence (AI) have significantly enhanced its diagnostic potential in such cases. This review explores the integration of AI techniques for analyzing specular microscopy images, emphasizing the shift from classical to advanced AI methods. We highlight AI-based methodologies—supervised and unsupervised learning—that have significantly enhanced the accuracy of in vivo human corneal endothelium analysis. The paper also discusses the challenges in data collection, emphasizing ethical considerations and the need for high-quality datasets. Additionally, we explore novel AI-derived metrics and their implications in enhancing diagnostic precision, particularly in Fuchs endothelial corneal dystrophy. The review concludes with insights into the future direction of AI in specular microscopy, highlighting its increasing relevance in ocular healthcare and the potential to overcome longstanding limitations in the field.
dc.description.researchareaProcesamiento y análisis de imágenes y señales
dc.format.extent17 páginas
dc.format.mimetypeapplication/pdf
dc.identifier.ark10.1371/journal.pdig.0001305
dc.identifier.citationMarrugo AG, Quintero F, Tello A, Prada AM, Galvis V, et al. (2026) Recent advances in corneal specular microscopy image analysis through artificial intelligence. PLOS Digital Health 5(3): e0001305. https://doi.org/10.1371/journal.pdig.0001305
dc.identifier.urihttps://hdl.handle.net/20.500.12585/14445
dc.publisherPLOS Digit Health
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dc.rightsCreative Commons
dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc610 - Medicina y salud::617 - Cirugía, medicina regional, odontología, oftalmología, otología, audiología
dc.subject.lembOphthalmology Cornea -- Diseases
dc.subject.lembArtificial intelligence in medicine
dc.subject.lembMedical imaging
dc.subject.lembCorneal specular microscopy
dc.subject.lembMachine learning
dc.subject.lembComputer-aided diagnosis
dc.subject.lembFuchs endothelial dystrophy
dc.subject.lembCorneal endothelium
dc.subject.lembDigital image processing
dc.subject.lembEye health
dc.subject.lembBiomedical technologies
dc.subject.ocde3. Ciencias Médicas y de la Salud::3B. Medicina Clínica::3B22. Oftalmología
dc.subject.odsODS 3: Salud y bienestar. Garantizar una vida sana y promover el bienestar de todos a todas las edades
dc.subject.proposalArtificial intelligence
dc.subject.proposalCorneal specular microscopy
dc.subject.proposalCorneal endothelium
dc.subject.proposalMachine learning
dc.subject.proposalDeep learning
dc.subject.proposalFuchs endothelial corneal dystrophy
dc.subject.proposalAutomated image analysis
dc.subject.proposalDiagnostic precision
dc.titleRecent advances in corneal specular microscopy image analysis through artificial intelligence
dc.typeArtículo de revista
dc.type.coarhttp://purl.org/coar/resource_type/c_18cf
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/article
dc.type.redcolhttp://purl.org/redcol/resource_type/ART
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dcterms.audienceComunidad académica
dspace.entity.typePublication
relation.isAuthorOfPublication651b0410-2e8f-4894-87bc-d3d803c92eab
relation.isAuthorOfPublication48ad794c-8c91-4bf7-9563-3b48428e1feb
relation.isAuthorOfPublication.latestForDiscovery651b0410-2e8f-4894-87bc-d3d803c92eab

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