Table 3. A summary of the research on prediction in the article.

Reference	Method	Optimisation strategy	Images (n)	Disease	Forecast content	Performance
de Sisternes et al61	L1-penalised Poisson model predictors	Piecewise-linear functionsand L1-penalised Poisson model	244	Dry AMD	Predicting the transition from early and intermediate age-related macular degeneration to advanced exudative macular degeneration	AUC 0.92
Schmidt-Erfurth et al62	Cox proportional hazards	Supervised setting, the least absolute shrinkage and selection operator	1095	Dry AMD	Predicting the risk of conversion to advanced AMD in patients with early age-related macular degeneration	CNV: AUC 0.68GA: AUC 0.80
Banerjee et al63	RNN	Many-to-manydata augmentation and long short-term memory	671	Dry AMD	Predicting the transition from early and intermediate age-related macular degeneration to advanced exudative macular degeneration	Internal validation: 3 months AUC 0.96, 21 months AUC 0.97External validation: 3 months AUC 0.82, 21 months AUC 0.68
Waldstein et al64	Deep learning	NA	1097	Dry AMD	Predicting the risk of conversion to advanced AMD in patients with early age-related macular degeneration	MNV: AUC 0.66MA: AUC 0.73
Rudas et al65	SLIVER-net	NA	4200	Dry AMD	Predicting progress in wet AMD	AUC 0.82
Bogunovic et al16	Random forest	NA	1095	Wet AMD	Predicting patient demand for anti-VEGF therapy	Low demand AUC of 0.7High demand AUC of 0.77
Romo-Bucheli et al66	DenseNet and RNN	Hyperbolic tangent	350	Wet AMD	Predicting patient demand for anti-VEGF therapy	Low demand AUC of 0.85High demand AUC of 0.81
Bogunović et al67	CNN	NA	228	Wet AMD	Predicting treatment needs and visual outcomes	The AUC for the predicted treat-and-extend group was 0.71The AUC for the visual outcome was 0.87
Liu et al68	GAN and pix2pixHD	‘Coach-to-fine’ training strategy	476	Wet AMD	Predicting short-term responses in patients treated with a single anti-vascular endothelial growth factor injection	92% of the synthesised OCT images were considered to be of sufficient quality for clinical interpretationpredicting post-treatment macular status (wet or dry) accuracy 85%
Zhao et al69	SSG-Net	Squeeze and excitation network and class activation	206	Wet AMD	Predicting whether patients will have a positive treatment response after short-term anti-VEGF therapy	Accuracy 84.6%, AUC 0.83, sensitivity 69.2%, specificity 100%

AMDage-related macular degenerationAUCarea under the curveCNNconvolutional neural networkCNVchoroidal neovascularizationGAgeographic atrophyGANgenerative adversarial networkMAmacular atrophyMNVmacular neovascularizationOCToptical coherence tomographyRNNrecursive neural network