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

Cicinelli, Maria Vittoria; Barlocci, Eugenio; Giuffrè, Chiara; Rissotto, Federico; Introini, Ugo; Bandello, Francesco

doi:10.1167/iovs.65.5.10

Invest. Ophthalmol. Vis. Sci.

2024

DOI: 10.1167/iovs.65.5.10

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

Maria Vittoria Cicinelli,

Eugenio Barlocci,

Chiara Giuffrè

et al.

Abstract: F. Integrating machine learning and traditional survival analysis to identify key predictors of foveal involvement in geographic atrophy.

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Cited by 2 publications

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Predict and Protect: Evaluating the Double-Layer Sign in Age-Related Macular Degeneration

Sivaprasad,

Chandra,

Sadda

et al. 2024

Ophthalmol Ther

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Introduction Advanced age-related macular degeneration (AMD) is a major cause of vision loss. Therefore, there is interest in precursor lesions that may predict or prevent the onset of advanced AMD. One such lesion is a shallow separation of the retinal pigment epithelium (RPE) and Bruch’s membrane (BM), which is described by various terms, including double-layer sign (DLS). Methods In this article, we aim to examine and clarify the different terms referring to shallow separation of the RPE and BM. We also review current evidence on the outcomes associated with DLS: firstly, whether DLS is predictive of exudative neovascular AMD; and secondly, whether DLS has potential protective properties against geographic atrophy. Results The range of terms used to describe a shallow separation of the RPE and BM reflects that DLS can present with different characteristics. While vascularised DLS appears to protect against atrophy but can progress to exudation, non-vascularised DLS is associated with an increased risk of atrophy. Optical coherence tomography (OCT) angiography (OCTA) is the principal method for identifying and differentiating various forms of DLS. If OCTA is unavailable or not practically possible, simplified classification of DLS as thick or thin, using OCT, enables the likelihood of vascularisation to be approximated. Research is ongoing to automate DLS detection by applying deep-learning algorithms to OCT scans. Conclusions The term DLS remains applicable for describing shallow separation of the RPE and BM. Detection and classification of this feature provides valuable information regarding the risk of progression to advanced AMD. However, the appearance of DLS and its value in predicting AMD progression can vary between patients. With further research, individualised risks can be confirmed to inform appropriate treatment.

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Predict and Protect: Evaluating the Double-Layer Sign in Age-Related Macular Degeneration

Sivaprasad,

Chandra,

Sadda

et al. 2024

Ophthalmol Ther

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The Discrepancy Between Visual Acuity Decline and Foveal Involvement in Geographic Atrophy

Cicinelli,

Barlocci,

Rissotto

et al. 2024

Ophthalmology Retina

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

Abstract: F. Integrating machine learning and traditional survival analysis to identify key predictors of foveal involvement in geographic atrophy.

Cited by 2 publications

References 32 publications

Predict and Protect: Evaluating the Double-Layer Sign in Age-Related Macular Degeneration

Predict and Protect: Evaluating the Double-Layer Sign in Age-Related Macular Degeneration

The Discrepancy Between Visual Acuity Decline and Foveal Involvement in Geographic Atrophy

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