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Make in India: Normative data for automated perimetry

BACKGROUND: The normative data set in authomated perimetry is predominantly of non-Indian origin and hence may not be an accurate basis for visual field analysis in Indian population. This video describes an attempt to create a native normative dataset for automated perimetry, which can then be fed...

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Detalles Bibliográficos
Autores principales: Israni, Neeraj, Thomas, Rwituja, Kochar, Shruti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114562/
https://www.ncbi.nlm.nih.gov/pubmed/35225588
http://dx.doi.org/10.4103/ijo.IJO_430_22
Descripción
Sumario:BACKGROUND: The normative data set in authomated perimetry is predominantly of non-Indian origin and hence may not be an accurate basis for visual field analysis in Indian population. This video describes an attempt to create a native normative dataset for automated perimetry, which can then be fed in our machines and be used as the normative database. PURPOSE: To formulate normative data and to increase domain knowledge of normative values for automated perimetry in Indian population of different age groups. SYNOPSIS: Cross-sectional study conducted on patients receiving outpatient care in a span of 3 years, which included 6586 healthy normal patients (13172 eyes) with vision 6/6 unaided or after refractive correction. The patients were tested with 30-2 SITA FAST threshold algorithm on Humphrey Field Analyzer Model no: 745i. Normative data was calculated on basis of age group ranging from 19-75 years categorized to every decade. Normal values were formulated on basis of perimetry performed on normal patients. HIGHLIGHTS: Our work on creating a native normative dataset may add value as well as increase the accuracy of perimetry analysis in Indian eyes. ONLINE VIDEO LINK: https://youtu.be/jqgC2Tn7HIg