Table 4.

Summary of studies using combination of modalities for predicting glaucoma progression

Year	First author	Aim	Outcome	Dataset	Model	Input	Output	Results
2013	Liu et al.[39]	Use structure and function for progression modelling, find fast progressors	Count of progressors	372 eyes	2D CT-HMM	VFI, MD, PSD, RNFL, GCC (RtVue-100)	Progression slopes	Structure degenerates faster earlier, then function degenerates faster (L shaped pattern)
2014	Yousefi et al.[91]	Compare progression performance of various MLC using RNFL and VF	AUC, SN, SP	180 eyes, 139 subjects	Various MLC (Bayesian net, Lazy K Star, Meta classification using regression, Meta ensemble selection, AD tree, RF and CART	VF: MD + PSD OCT: RNFL (Spectralis)	Progression	RF (AUC: 0.88 [0.91–0.85]) performed best when both RNFL + VF features. lazy K star performed best with only RNFL (AUC: 0.88 [0.91–0.86]) and VF (AUC: 0.82 [0.86–0.79]) features
2019	Garcia et al.[92]	Forecast IOP, MD, PSD	Error %, RMSE	OHTS study 1047 subjects (2806 eyes)	KF based models	MD, PSD, IOP	5-year forecasts	KF-OHTN forecast MD values 60 months into the future within 0.5 dB of the actual value for 696 eyes (32.8%), 1.0 dB for 1295 eyes (61.0%), and 2.5 dB for 1980 eyes (93.2%). Among the 5 forecasting algorithms tested, KF-OHTN achieved the lowest RMSE (1.72 vs. 1.85-4.28) for MD values 60 months into the future. For IOP, KF-OHTN forecast within 1.0 mmHg of the actual value in 560 eyes (26.4%; 95% CI: 24.5%–28.3%), within 2.5 mmHg of the actual value in 1255 eyes (59.1%; 95% CI: 57.0%–61.2%) and within 5 mmHg of the actual value in 1854 eyes (87.3%; 95% CI: 85.9%–88.7%)
2019	Garcia et al.[93]	Forecast IOP, MD, PSD	Error %, RMSE	263 eyes of NTG subjects	KF based models	MD, PSD, IOP	2-year forecasts	MD at 0.5, 1.0 and 2.5 dBs of the actual value for 78 eyes (32.2%), 122 eyes (50.4%) and 211 eyes (87.2%). When forecasting MD, KF-NTG (RMSE=2.71) and KF-HTG (RMSE=2.68)
2020	Sedai et al.[94]	ML model to predict RNFL from multimodal data	Mean error	1089 participants	3D CNN + different MLR (GBM, LR, LR-lasso, SVM, RVM)	Multimodal data	OCT	Mean error: 1.10±0.60 µm, 1.79±1.73 µm and 1.87±1.85 µm in eyes of healthy, glaucoma suspect and glaucoma participants. ML outperforms the linear LTBE model
2021	Dixit et al.[95]	RNN for VF progression using corresponding baseline clinical data (CDR, CCT, IOP)	AUROC	672, 123 VF, 213,254 eyes and 350,437 clinical data >4 VF	RNN (LSTM)	VF, VF + clinical data	Progression	LSTM accuracy: 91%–93% VF + clinical data: (AUROC: 0.89–0.93) versus VF alone: (AUROC: 0.79–0.82). LSTM outperforms MD slope, PLR, and VFI slope
2022	Lee et al.[96]	Predict RNFL thinning	MAE	712 participants	RF + Shapley additive explanation	Eleven features were selected as input variables: age, sex, highest IOP during the initial 6 months, glaucoma surgery during the initial 6 months, mean LCCI, global peripapillary CT, global RNFL, VF mean deviation (MD), VF pattern standard deviation (PSD), AXL, and CCT	Rate of RNFL progression	MAEs for the RF, regression, and decision tree models were 0.075, 0.115 and 0.128. Based on the decision tree, higher IOP (>26.5 mmHg), greater laminar curvature (>13.95) and thinner peripapillary choroid (≤117.5 µm) were the 3 most important determinants affecting the rate of RNFL thinning
2022	Tarcoveanu et al.[8]	Evaluate classification algorithms	Accuracy	50 subjects	Multilayer perceptron, RF, random tree, C4.5, kNN, SVM and NNGE	Age, gender, systemic history + ocular measurements (IOP, CDR, CCT) + lab values (HbA1C) for glaucoma evolution VFI, MD, PSD, and RNFL for glaucoma progression	Glaucoma evolution and progression	Multilayer perceptron and RF have >90% accuracy. The same pipeline can additionally predict DR in glaucoma patients

AD=Alternating decision tree, AUC/AUROC=Area under the area under the receiver operating characteristic curve, AXL=Axial length, CART=Classification and regression tree, CDR=Cup disc ratio, CCT=Central corneal thickness, CNN=Convolutional neural network, CT=Choroidal thickness, CT-HMM=Continuous time hidden Markov model, DR=Diabetic retinopathy, GBM=Gradient boosted machine, GCC=Ganglion cell complex, IOP=Intraocular pressure, KF=Kalman filtering , kNN=k-nearest neighbour, LTBE=Linear trend based estimation, LCCI=Lamina cribrosa curvature index, LSTM=long short term memory, MAE=Mean absolute error, MD=Mean deviation, ML=Machine learning, MLR=Machine learning regressor, NNGE=Nonnested generalized exemplars , OHTS=Ocular hypertension study, PLR=Pointwise linear regression, PSD=Pattern standard deviation, RF=Random forest, RMSE=Root mean square error, RNFL=Retinal nerve fibre layer thickness, RNN=Recurrent neural network, RVM=Relevance vector machine regressor, SVM=Support vector machine VF=Visual field, VFI=Visual field index, Hb=Hemoglobin