Table 3.

Summary of studies using visual fields for predicting glaucoma progression

Year	First author	Aim	Outcome	Dataset	Model	Input	Output	Results
2012	Goldbaum et al.[63]	ML model (POP) to define VF progression	Percentage of eyes progressing	2085 subjects	POP model based on VIM versus GPA, MD and VFI	VF	VF progression	POP has similar performance to GPA/MD/VFI in glaucoma suspects but performs better in subjects with glaucoma and those with documented glaucoma
2014	Yousefi et al.[64]	Define hierarchical approach to VF analysis	Percentage of eyes progressing	939 eyes (677 subjects) abnormal, 1146 eyes (721 subjects) normal	(ML models) GEM, VIM versus GPA, MD and VFI	VF	VF progression	GEM: 28.9%, VIM: 26.6%, GPA: 19.7%, MD: 16.9%, VFI: 14.1%
2018	Yousefi et al.[65]	Predict progression using different methods	Time to progression	3 datasets: 2085 eyes to identify patterns, no change/test-retest data: 133 eyes (10 times/10 weeks) 270 eyes to validate	GEM versus conventional models	VF	Time to progression	Time to detect progression in 25% of the eyes: MD: 5.2 (95% CI: 4.1–6.5) years; region-wise: 4.5 (4.0–5.5) years, point-wise: 3.9 (3.5–4.6) years, GEM: 3.5 (3.1–4.0) years. When more visits added 6.6 (5.6–7.4) years, 5.7 (4.8–6.7) years, 5.6 (4.7–6.5) years, and 5.1 (4.5–6.0) years for global, region-wise, point-wise and GEM
2019	Wen et al.[17]	Predict future HVF	PMAE	32,443 VF	CNN: Cascade 5	VF raw sensitivity values	52-point raw sensitivity at 0.5–5.5 years	Overall point-wise PMAE (dB): 2.47 (95% CI: 2.45–2.48)
2019	Berchuck et al.[73]	Predict rate of progression	MAE	29,161 VF	VAE	VF	VF	VAE predicts higher progression than MD at 2/4 years (25%–35% vs. 9%–15%), VAE also better than PWE error at visit 8 (5.14 dB vs. 8.07 dB)
2019	Wang et al.[70]	VF progression	Kappa, accuracy	12,217 eyes, 7360 patients	Archetypal analysis[67]	5 reliable VF, 5 years follow up, 6-month interval	VF progression	Clinical validation cohort (397 eyes with 27.5% of confirmed progression), the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P<0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59) and PoPLR (0.26 and 0.60)
2019	Park et al.[74]	Predict future HVF	RMSE	Training: 1408 eyes, 281 eyes test	RNN	Five consecutive VF	52-point TDV values	RNN outperformed OLR and gave an overall prediction error (RMSE) of 4.31±2.54 dB versus 4.96±2.76 for the OLR model (P<0.001)
2020	Yousefi et al.[71]	AI dashboard for VF progression	SN, SP	31,591 VF, 8077 subjects	Combination of PCA + t-distributed stochastic neighbor embedding (tSNE)	VF	VF progression	SP for detecting “likely nonprogression” was 94% and SN for detecting “likely progression” was 77%
2021	Saeedi et al.[66]	MLC for VF progression	Accuracy, SN, PPV, class bias	90,713 VF, 13,156 eyes	ML classifiers versus conventional progression algorithms	VF	VF progression	6 ML classifiers involved: Logistic regression, random forest, extreme gradient boosting, support vector classifier, CNN, fully connected neural network. 87%–91% accuracy, SN: 0.83–0.88, SP: 0.92–0.96
2021	Shuldiner et al.[67]	ML can predict VF progression	AUC	175,786 VF, 22,925 initial VF, 14,217 subjects >5 reliable VF	Various ML classifiers like SVM, ANN, random forest and naive bayes classifier	VF	VF progression	SVM model (AUC: 0.72 [95% CI: 0.70–0.75]) versus ANN (AUC: 0.72), random forest (AUC: 0.70), logistic regression (AUC: 0.69) and naive Bayes classifiers (AUC: 0.68). Older age and higher PSD associated with progression. 2 VF versus 1 VF model no difference
2022	Eslami et al.[13]	CNN/RNN for estimating VF changes	PMAE	24–4 VF CNN: 54,373, 7472 subjects RNN: 24,430, 1809 subjects	CNN and RNN	VF	52-point VF values	CNN: 2.21–2.24 dB, RNN: 2.56–2.61 dB, large errors in identifying those with worsening and failed to outperform no change model
2022	Chen et al.[75]	VF progression	Progression yes/no	7428 eyes, 3871 patients	Elastic-net cox regression model	First VF, age, gender, laterality, and MD at baseline	Sample size required for appropriate trial effect size	13% progressed over 5 years, for a trial length of 3 years and effect size of 30%, the number of patients required was 1656 (95% CI: 1638–1674), 903 (95% CI: 884–922) and 636 (95% CI: 625–646) for the entire cohort, the subgroup and the model-selected patients, respectively
2022	Yousefi et al.[7]	VF progression	Pattern of loss	2231 VF, 205 eyes, 176 OHTS subjects over 16 years	Deep archetypal analysis[68]	VF	Pattern of loss	18 machine-identified patterns of VF loss similar to 13 expert-identified patterns. Most prevalent expert-identified patterns included partial arcuate, paracentral and nasal step defects and most prevalent machine-identified patterns included temporal wedge, partial arcuate, nasal step and paracentral VF defects
2022	Shon et al.[72]	VF progression by AI versus linear models	AUROC	9212 eyes, 6047 subjects >4 years	VF block: CNN	Three VF as 3D tensor	VF progression over 3 years	CNN: AUROC: 0.864, SN: 0.42, SP: 0.95; PLR: AUROC: 0.611, SN: 0.28, SP: 0.84

AGIS=Advanced glaucoma intervention study scoring, AUC/AUROC=Area under the receiver operating characteristic curve, CIGTS=Collaborative initial glaucoma treatment study scoring, CNN=Convolutional neural network, GEM=Gaussian mixture model-expectation maximization, GPA=Glaucoma progression analysis, MD=Mean deviation, OLR=Ordinary linear regression, PCA=Principal component analysis, MAE=Mean absolute error, PMAE=Pointwise MAE, RNN=Recurrent neural network, SN=Sensitivity, SP=Specificity, SVM=Support vector machine, TDV=Total deviation values, VAE=Variational auto-encoder, VIM=Variational Bayesian independent component analysis mixture model, VF=Visual field, VFI=Visual field index, POP=Permutation of pointwise, HVF=Humphrey VF, AI=Artificial intelligence, PPV=Positive predictive value, RMSE=Root mean square error, OHTS=Ocular hypertension treatment study, PLR=Pointwise linear regression, tSNE=t-distributed stochastic neighbor embedding, CI=Confidence interval