FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3011490500> ?p ?o ?g. }

• W3011490500 endingPage "1178" @default.
• W3011490500 startingPage "1170" @default.
• W3011490500 abstract "Purpose To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss. Design Retrospective, cross-sectional, longitudinal cohort study. Participants Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models. Method We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a “pipeline” that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an “AI-enabled glaucoma dashboard.” We used density-based clustering and the VF decomposition method called “archetypal analysis” to annotate the dashboard. Finally, we used 2 separate benchmark datasets—one representing “likely nonprogression” and the other representing “likely progression”—to validate the dashboard and assess its ability to portray functional change over time in glaucoma. Main Outcome Measures The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma. Results After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting “likely nonprogression” was 94% and the sensitivity for detecting “likely progression” was 77%. Conclusions The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression. To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss. Retrospective, cross-sectional, longitudinal cohort study. Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models. We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a “pipeline” that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an “AI-enabled glaucoma dashboard.” We used density-based clustering and the VF decomposition method called “archetypal analysis” to annotate the dashboard. Finally, we used 2 separate benchmark datasets—one representing “likely nonprogression” and the other representing “likely progression”—to validate the dashboard and assess its ability to portray functional change over time in glaucoma. The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma. After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting “likely nonprogression” was 94% and the sensitivity for detecting “likely progression” was 77%. The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression." @default.
• W3011490500 created "2020-03-23" @default.
• W3011490500 creator A5010480125 @default.
• W3011490500 creator A5011529236 @default.
• W3011490500 creator A5016092761 @default.
• W3011490500 creator A5032325357 @default.
• W3011490500 creator A5034761457 @default.
• W3011490500 creator A5036981123 @default.
• W3011490500 creator A5047341378 @default.
• W3011490500 creator A5059589308 @default.
• W3011490500 creator A5062453784 @default.
• W3011490500 creator A5084071114 @default.
• W3011490500 date "2020-09-01" @default.
• W3011490500 modified "2023-10-10" @default.
• W3011490500 title "Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence–Enabled Dashboard" @default.
• W3011490500 cites W1514230101 @default.
• W3011490500 cites W1783352676 @default.
• W3011490500 cites W1963492540 @default.
• W3011490500 cites W1999677058 @default.
• W3011490500 cites W2020112862 @default.
• W3011490500 cites W2022187553 @default.
• W3011490500 cites W2023895379 @default.
• W3011490500 cites W2029622432 @default.
• W3011490500 cites W2031263891 @default.
• W3011490500 cites W2034588286 @default.
• W3011490500 cites W2039455901 @default.
• W3011490500 cites W2045519105 @default.
• W3011490500 cites W2063316149 @default.
• W3011490500 cites W2065289190 @default.
• W3011490500 cites W2066680326 @default.
• W3011490500 cites W2068089215 @default.
• W3011490500 cites W2070086505 @default.
• W3011490500 cites W2103804004 @default.
• W3011490500 cites W2133671386 @default.
• W3011490500 cites W2138395977 @default.
• W3011490500 cites W2140591816 @default.
• W3011490500 cites W2164019812 @default.
• W3011490500 cites W2171392732 @default.
• W3011490500 cites W2319331509 @default.
• W3011490500 cites W2331210603 @default.
• W3011490500 cites W2349932203 @default.
• W3011490500 cites W2414083061 @default.
• W3011490500 cites W2734838012 @default.
• W3011490500 cites W2768095311 @default.
• W3011490500 cites W2778784225 @default.
• W3011490500 cites W2790876277 @default.
• W3011490500 cites W2792929794 @default.
• W3011490500 cites W2809659847 @default.
• W3011490500 cites W2884147047 @default.
• W3011490500 cites W2912084594 @default.
• W3011490500 cites W2914812828 @default.
• W3011490500 cites W4250320908 @default.
• W3011490500 cites W84395270 @default.
• W3011490500 doi "https://doi.org/10.1016/j.ophtha.2020.03.008" @default.
• W3011490500 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7483368" @default.
• W3011490500 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32317176" @default.
• W3011490500 hasPublicationYear "2020" @default.
• W3011490500 type Work @default.
• W3011490500 sameAs 3011490500 @default.
• W3011490500 citedByCount "18" @default.
• W3011490500 countsByYear W30114905002020 @default.
• W3011490500 countsByYear W30114905002021 @default.
• W3011490500 countsByYear W30114905002022 @default.
• W3011490500 countsByYear W30114905002023 @default.
• W3011490500 crossrefType "journal-article" @default.
• W3011490500 hasAuthorship W3011490500A5010480125 @default.
• W3011490500 hasAuthorship W3011490500A5011529236 @default.
• W3011490500 hasAuthorship W3011490500A5016092761 @default.
• W3011490500 hasAuthorship W3011490500A5032325357 @default.
• W3011490500 hasAuthorship W3011490500A5034761457 @default.
• W3011490500 hasAuthorship W3011490500A5036981123 @default.
• W3011490500 hasAuthorship W3011490500A5047341378 @default.
• W3011490500 hasAuthorship W3011490500A5059589308 @default.
• W3011490500 hasAuthorship W3011490500A5062453784 @default.
• W3011490500 hasAuthorship W3011490500A5084071114 @default.
• W3011490500 hasBestOaLocation W30114905002 @default.
• W3011490500 hasConcept C118487528 @default.
• W3011490500 hasConcept C119857082 @default.
• W3011490500 hasConcept C154945302 @default.
• W3011490500 hasConcept C185798385 @default.
• W3011490500 hasConcept C205649164 @default.
• W3011490500 hasConcept C2522767166 @default.
• W3011490500 hasConcept C2778527774 @default.
• W3011490500 hasConcept C33499554 @default.
• W3011490500 hasConcept C41008148 @default.
• W3011490500 hasConcept C58640448 @default.
• W3011490500 hasConcept C71924100 @default.
• W3011490500 hasConcept C73555534 @default.
• W3011490500 hasConceptScore W3011490500C118487528 @default.
• W3011490500 hasConceptScore W3011490500C119857082 @default.
• W3011490500 hasConceptScore W3011490500C154945302 @default.
• W3011490500 hasConceptScore W3011490500C185798385 @default.
• W3011490500 hasConceptScore W3011490500C205649164 @default.
• W3011490500 hasConceptScore W3011490500C2522767166 @default.
• W3011490500 hasConceptScore W3011490500C2778527774 @default.
• W3011490500 hasConceptScore W3011490500C33499554 @default.
• W3011490500 hasConceptScore W3011490500C41008148 @default.
• W3011490500 hasConceptScore W3011490500C58640448 @default.

• next