Integrating Machine Learning and Traditional Survival Analysis to Identify Key Predictors of Foveal Involvement in Geographic Atrophy.

Journal: Investigative ophthalmology & visual science

PMID: 38709525

Abstract

PURPOSE: The purpose of this study was to investigate the incidence of foveal involvement in geographic atrophy (GA) secondary to age-related macular degeneration (AMD), using machine learning to assess the importance of risk factors.

Authors

Maria Vittoria Cicinelli

School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.
Eugenio Barlocci

School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.
Chiara Giuffrè

Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Federico Rissotto

School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.
Ugo Introini

Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Francesco Bandello

Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, via Olgettina 60, 20132, Milan, Italy.

Keywords

Aged Aged, 80 and over Female Fluorescein Angiography Follow-Up Studies Fovea Centralis Geographic Atrophy Humans Incidence Machine Learning Male Middle Aged Retrospective Studies Risk Factors Survival Analysis Tomography, Optical Coherence Visual Acuity

External Resources

View on PubMed Access via DOI PubMed (38709525)

Integrating Machine Learning and Traditional Survival Analysis to Identify Key Predictors of Foveal Involvement in Geographic Atrophy.

Abstract

Authors

Keywords

External Resources

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