Table 2.

Summary of studies using optical coherence tomography for predicting glaucoma progression

Year	First Author	Aim	Outcome	Dataset	Model	Input	Output	Results
2013	Kim et al.[40]	FA of OCT	AUC	NA	FA, WFA, FFA	2D OCT images	Glaucoma progression	AUC: 0.82 for FA, AUC: 0.88 for multi class classification. FA better than WFA and FFA
2018	Christopher et al.[41]	SSOCT features for glaucoma progression (defined on 3 expert’s examinations of SFF)	AUC	28 normal, 93 glaucoma	PCA	Triton SSOCT	RNFL PCA features	RNFL PCA (AUC: 0.74) outperformed mean cpRNFL from Spectralis SDOCT (AUC: 0.55), SAP MD (AUC: 0.58), FDT MD (AUC: 0.52)
2021	Lazaridis et al.[45]	TDOCT to SDOCT and then VF progression	HR	361 subjects	GAN	TDOCT	SDOCT	95% limits of agreement were between TD OCT and SD OCT were 26.64 and−22.95; between synthesized SD-OCT and SD-OCT were 8.11 and−6.73; and between SD OCT and SD OCT were 4.16 and−4.04. HR for RNFL slope in cox regression modeling for time to incident VF progression was 1.09 (95% CI: 1.02–1.21; P=0.035) for TD OCT and 1.24 (95% CI: 1.08–1.39; P=0.011) for synthesized SD-OCT
2020	Normando et al.[46]	DARC detection on OCT using CNN versus graders	Accuracy, SN, SP	60 subjects	ZNCC + MobileNetV2	Spectralis OCT images with DARC	DARC detection	DARC count increases in those who progress. CNN accuracy (97.0%), SN (91.1%) and SP (97.1%)
2021	Bowd et al.[43]	RNFL-based progression (defined by 3 expert’s examination of SFF)	SN, SP	342 subjects, >3 years, >4 OCT visits	DL-AE	RNFL thickness from Spectralis OCT	Glaucoma progression	DL-AE (SN: 0.90) outperformed global cpRNFL thickness (SN: 0.63)
2021	Nouri- Mahdavi et al.[47]	OCT can predict VF progression (3 locations, ≤−1 dB/year with P<0.01)	AUC	104 subjects with >3 year follow up and >5 VF	ElasticNet (ENR) + other ML classifiers (naive Bayes, random forests, and SVM)	Spectralis OCT + demographic/clinical factors	Glaucoma progression	ENR selected rates of change of supertemporal RNFL sector and GCIPL change rates in 5 central super pixels and at 3.4° and 5.6° eccentricities as the best predictor subset (AUC=0.79±0.12). Best ML (naive bayes classifier) predictors consisted of baseline superior hemi macular GCIPL thickness and GCIPL change rates at 3.4° eccentricity and 3 central super pixels (AUC=0.81±0.10) GCIPL models better than RNFL models
2020	Raja et al.[42]	OCT to detect progression	Accuracy, F1	AFIO dataset	RAG-Net v2 + SVM	Topcon 3D OCT	Healthy, early, advanced glaucoma	F1 score of 0.9577 for diagnosing glaucoma, a mean dice coefficient score of 0.8697 for extracting the RGC regions, and an accuracy of 0.9117 for grading glaucomatous progression
2021	Asaoka et al.[44]	Predict VF and VF progression using OCT	RMSE	Cross-sectional: 746 eyes, 478 subjects Longitudinal: 1146 eyes, 676 subjects	Latent space linear regression and deep learning (VGG16) i.e., LSLR-DL	RS3000 Nidek OCT 10–2 HVF pairs for cross-sectional mode, 24–2 longitudinal mode	68 - point sensitivity (10–2 VF) and 52point sensitivity of 24–2 VF (eighth in series)	Mean RMSE in the cross-sectional prediction was 6.4 dB and was between 4.4 dB (VF tests 1 and 2) and 3.7 dB (VF tests 1–7) in the longitudinal prediction
2022	Mariottoni et al.[19]	Detect progression using OCT (3 graders defined progression)	AUC, SN, SP	14,034 scans from 816 eyes (462 people)	CNN	RNFL thickness using Spectralis OCT	Glaucoma progression	AUC: 0.938 (0.921–0.955), SN: 87.3% (83.6%–91.6%) SP: 86.4% (79.9%–89.6%)

AFIO: Armed forces institute of ophthalmology, AUC=Area under the receiver operating characteristic curve, CNN=Convolutional neural network, DARC=Detection of apoptosis retinal cells, ENR=ElasticNet regression, FA=Fractal analysis, FDT=Frequency doubling perimetry, FFA=Fast-fourier analysis, GAN=Generative adversarial networks, GCIPL=Ganglion cell-inner plexiform layer thickness, HR=Hazard ratio, MD=Mean deviation, ML=Machine learning, NA=Not available, OCT=Optical coherence tomography, PCA=Principal component analysis, RAG-Net=Retinal analysis and grading network, RNN=Recurrent neural network, RGC=Retinal ganglion cells, RMSE=Root mean square error, RNFL=Retinal nerve fibre layer, RNFL/cpRNFL=RNFL thickness, SDOCT=spectral domain OCT, SAP=Standard automated perimetry, SFF=Stereo fundus photos, SN=Sensitivity, SP=Specificity, SS-OCT=Swept source OCT, SVM=Support vector machine, TDOCT=Time domain OCT, TDV=Total deviation values, VAE=Variational auto-encoder, VIM=Variational bayesian independent component analysis mixture model, VF=Visual field, VFI=VF index, WFA=Wavelet-fourier analysis, DL-AR=DL-auto encoder, ZNCC=Zero normalized cross-correlation, SSOCT=Swept source OCT, 2D=2-dimensional, 3D=3-dimensional, ZNCC=Zero Normalised Cross-Correlation, VGG16=Visual Geometry Group-16, LSLR-DL=latent space linear regression and deep learning