Table 2.
Learning algorithms for age-related macular degeneration progression.
| Study | Description | Dataset | Results |
|---|---|---|---|
| Banerjee et al.23 a | Hybrid sequential prediction model "Deep Sequence" integrating OCT imaging features, demographic and visual variables with an RNN model to predict risk of exudation (progression to neovascular AMD) within 3 to 21 months | HARBOR: 671 fellow eyes | AUC:0.96± 0.02 (3 months)0.97± 0.02 (21 months) |
| Bhuiyan et al. 34 a | Color fundus photos and 12-step severity scale class used with demographic data in logistic tree model to predict progression to advanced dry, neovascular, or all late AMD in 1 or 2 years (yrs). | AREDS >4600 color fundus images | ALL advanced AMDSe: 0.91 (1 yr), 0.92 (2 yr)Sp: 0.85 (1 yr), 0.84 (2 yr)ACC: 0.86 (1 yr), 0.86 (2 yr) |
| Wu et al. 35 | Cox proportional hazards models with LOOCV to compare fundus versus OCT versus both inputs for neovascular and advanced dry AMD progression in 36 months | LEAD: 280 eyes from 140 participants. OCT B-scans, fundus photos. | AUC: 0.85 |
| Wu et al. 36 | Cox proportional hazards model with LOOCV to examine added predictive value of PSD and LLD to fundus data for progression to GA, nascent GA and neovascular AMD within 36 months | LEAD: 280 eyes from 140 participants. fundus photos. | AUC: 0.80 |
| Yan et al.37 a | Genotypes and color fundus images used to predict progression to advanced dry or neovascular AMD within range of 2–7 years with a modified deep convolutional neural network | AREDS > 31,000 fundus images from 1351 subjects | AUC: 0.85–0.86 |
| Yim et al. 38 | DL model based on three-dimensional SD-OCT images and automatic tissue maps combined for neovascular AMD prediction within 6 months | Internal retrospective cohort 2795 patients. OCT | AUC: 0.745 (conversion scan ground truth), 0.886 (first injection ground truth) |
| Hallak et al. 24 | Mixed methods to determine associations between variables and progression to neovascular AMD. Bivariate analysis for genetic variants and LASSO regression for OCT imaging features decided the variables included alongside demographic data in survival analysis and Cox proportional hazards regression. | HARBOR: 686 fellow eyes with non neovascular AMD at baseline | Female sex (HR, 1.57; 95% CI, 1.11–2.20)Drusen area within 3 mm of the fovea (HR, 1.45; 95% CI, 1.24–1.69)Mean drusen reflectivity (HR, 3.97; 95% CI, 1.11–14.18 |
| Rivail et al. 39 | Deep Siamese network capturing time-specific features on longitudinal data to predict advanced dry AMD in 6, 12, and 18 months. | Internal: 3308 OCT B-scans from 221 patients (420 eyes) | AUC:6 months: 0.75312 months: 0.78418 months: 0.773 |
| Russakoff et al. 40 | Comparison of two deep convolutional neural networks for neovascular AMD risk prediction over 2 years (17–27-months follow-up). | Internal: 71 eyes, 71 subjects (progressors =31). 9088 OCT B-scans from two devices. | AUC:VGG16: 0.87AMDnet: 0.91 |
| Burlina et al. 41 | 3 DCNN models developed to estimate 5-year risk of progression to neovascular and GA based on 9-step AREDS severity scale | AREDS< 6000 fundus images across 9 classifications | PE:Soft model: 0.038Hard model: 0.035Regressed: 0.053 |
| Schmidt-Erfurth et al. 42 | Demographic, genetic, and image features input to Cox proportional hazards models with 10-fold cross validation to predict neovascular AMD or GA in 2 years | HARBOR: 495 eyes (progressors = 159 eyes) SD-OCT | Se: 0.8 (CNV), 0.8 (GA)Sp: 0.46 (CNV), 0.69 (GA)AUC: 0.68 (CNV), 0.8 (GA) |
| Seddon et al. 12 | Genetic, demographic, environmental, and image features input to Cox proportional hazards models to predict neovascular AMD and/or GA at any follow-up visit within 12 years | AREDS: fundus images, 2951 subjects (834 progressors) | AUC:0.911 (All AMD), 0.923 (GA), 0.896 (neovascular AMD) |
| Chiu et al.43 a | Demographic and environmental features included in logistic model producing a risk scoring system for neovascularization or central GA by end of study timeframe (12 years) | AREDS: fundus images from 4507 participants (1185 progressors) | Se: 0.876Sp: 0.736 |
| de Sisternes et al. 25 | Automated pipeline for segmentation and extraction of longitudinal image features used in L1-penalized Poisson model predicting neovascular AMD progression within 5 years | Private: 2146 SD-OCT scans of 330 eyes of 244 patients (36 eyes progressed) | AUC: 5 yr: 0.7411 months:0.9216 months:0.8618 months:0.748 months:0.79 |
| Seddon et al.15 a | Output from Cox proportional hazards regression, including demographic, environmental and genetic features, used for predictive algorithm of neovascular AMD and GA within 5 or 10 years | AREDS: fundus photos from 2937 individuals (819 progressors) | AUC:5 yr: 0.88510 yr: 0.915 |
Se: sensitivity; Sp: specificity; Acc: accuracy; AUC: area under the receiver operating curve; PE: prediction error.
aValidation performed on external dataset.