Accelerating precision ophthalmology: recent advances

Figures & data

Table 1. A summary of some recent AI studies in glaucoma over the last three years.

Studies	Year of publication	AI model	Function	Results	Conclusions
Hemelings R.et al [12].	2021	ResNet	Detection of vertical cup-disc ratio (VCDR) in glaucoma using fundus image	AUC 0.88 [95% CI 0.85–0.9]	First irrefutable evidence that deep learning can detect glaucoma from fundus image regions outside the optic nerve head.
Han X.et al [13].	2021	Convolutional neural network (CNN)	Assessments of the optic nerve head using photographs	200 loci associated with both VCDR and VDD were identified	The potential for CNN in identifying potential genes in glaucoma.
Chiang M.et al [20].	2021	CNN based on ResNet-50	Detection of angle closure in EyeCam goniophotographs	AUC of 0.969 (95% CI 0.961–0.976) in detecting angle closure WITH CNN MODEL Human graders demonstrated a range of performance in the same task with most true positive rates (range = 0.701–0.973; P = 0.01) versus false positive rates (range = 0.042–0.219; P = 0.31) with higher levels of clinical experience	CNN classifier can effectively detect angle closure in goniophotographs, and is comparable to the performance of an experienced glaucoma specialist.
Liu B.et al [15].	2022	ECSD-Net (unsupervised model)	Segmentation of the optic disc and cup	Combined segmentation result and classification result for the final glaucoma classification is better than the single-use of the segmentation network or the classification network.	This method can assist clinicans in screening and diagnosis of glaucoma that has the potential for real world applications
Thakoor KA.et al [17].	2021	CNN	Detection of glaucoma in OCT	160 random concept experiments conducted to assess test conduct activation vectors (TCAV) High TCAV scores achieved comparing human vs CNN	Pre-trained CNNs followed by fine-tuned layers enhanced robustness when tested on new dataset. Critical parameters to detect glaucoma demonstrated by TCAVs show RNFL/RCGP probability maps as well as RGCP thickness maps are most important
Lee J.et al [18].	2020	Hybrid deep learning model (HDLM)	Detection of macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs) in SD-OCT	Trained algorithm had a correlation coefficient r=0.739; p <0.001 and an AUC of 0.918 95% CI (0.898–0.939) for mGCIPL thickness prediction.	The trained HDLM algorithm showed greater capability for mGCIPL thickness prediction for RNFLPs.
Wang M.et al [19].	2020	NMF (unsupervised model)	Assessment of the spatial patterns of retinal nerve fiber layer thickness (RNFLT) in visual field (VF) and OCT	16 distinct RNFLT patterns were determined by NMF and improved VF prediction when compared to sectoral RNFLTS (P<0.001)	The patterns significantly improved the prediction of sensitivities within the visual field mapping

Table 2. Some recent AI studies in AMD over the last three years.

Studies	Year of publication	AI model	Function	Results	Conclusions
Ji Z.et al [41].	2018	A deep network	Segmentation of geographic atrophy (GA) in OCT	2 image data sets used Mean overlap ratio 86.94% ± 8.75% and 81.66% ± 10.93% respectively Absolute area difference (AAD) 11.49% ± 11.5% and 8.30% ± 9.09%, respectively Correlation coefficients 0.9857 (1^st dataset) 0.9952 (2^nd dataset)	Proposed algorithm can improve >5%-10% segmentation accuracy compared to other algorithms
Saha S. et al [42].	2019	Deep convolution neural networks (CNN)	Automated analysis of AMD biomarkers in OCT	3 convolutional neural networks (CNN) compared. Inception- V3 accuracy (IHRF vs hRF vs SDD) 89%, 88% and 84% ResNet50 accuracy 88% vs 87% vs 81% InceptionResNet50 89%, 87% and 85%, respectively	87% accuracy in identifying early AMD biomarkers
Zhang, G. et al [43].	2021	A modified U-Net	Segmentation of macular atrophy in OCT	Similarity between deep learning model and human specialist grading: median Dice similarity coefficient (DSC) 0·96 [IQR 0·10]; intraclass correlation coefficient [ICC] 0·93] vs human graders (DSC 0·80 [0·28]; ICC 0·79) Accuracy of segmentation for three features of GA: retinal pigment epithelium loss (median DSC 0·95 [IQR 0·15]), overlying photoreceptor degeneration (0·96 [0·12]), and hypertransmission (0·97 [0·07])	Deep learning composite model for segmentation of GA acheives performance at a similar level to manual specialist assessment
Liefers, B. et al [44].	2021	CNN	Segmentation of 13 features in AMD	Model vs human observes DSC: 0.63 ± 0.15, vs 0.61 ± 0.17 Mean ICC 0.66 ± 0.22, compared with 0.62 ± 0.2	Automatic segmentation quality acheives results that match experienced graders and exceeding human performance for some features

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