Table 3.

A summary table of artificial intelligence (AI) applications in segmentation of corneal endothelium and nerves, in reverse chronological order

Year	Authors	Imaging modality	Sample size (eyes)	Study population	Outcome measures	AI algorithms	Diagnostic performance	Validation model
Corneal endothelium
2023	Karmakar et al. [75]	Konan CellCheck XL	612	Healthy and diseased eyes	Segmentation of endothelial cells	Mobile-CellNet CNN	Mean absolute error: 4.06%	Hold-out validation
2022	Qu et al. [136]	IVCM	97	Healthy, FECD and corneal endotheliitis eyes	Segmentation of endothelial cells	CNN	PCC: 0.818–0.932	Hold-out validation
2020	Canavesi et al. [77]	GDOCM	10	Eye bank	Segmentation of endothelial cells	CNN	Correlation: 0.91–0.94	Cross validation
2019	Bennett et al. [80]	JDS Uniphase, TOMEY TMS-5	10	Healthy eyes	Evaluation of corneal thickness	CNN	RMSE: 0.045–0.048 Acc: 84.82%–89.26%	Hold-out validation
2019	Vigueras-Guillén et al. [137]	Topcon SP-1P	738	Patients with Baerveldt glaucoma device and DSAEK	Segmentation of endothelial cells	CNN	Mean absolute error: 4.32%–11.74%	Hold-out validation
2019	Daniel et al. [70]	Topcon SP-3000	385	Database of healthy, endothelial disease and corneal graft eyes	Segmentation of endothelial cells	U-Net CNN	PCC: 0.96, Sens: 0.34% Precis: 0.84%	Hold-out validation
2018	Fabijańska et al. [73]	Specular microscopy	30	Dataset of endothelial cell images	Evaluation of corneal thickness	U-Net CNN	AUC: 0.92, Dice: 0.86 Mean absolute error: 4.5%	Hold-out validation
2018	Vigueras-Guillén et al. [76]	Topcon SP-1P	103	Dataset of endothelial cell images	Evaluation of corneal thickness	SVM	Precis: P < 0.001 Acc: P < 0.001	Cross validation
Corneal nerves
2023	Li et al. [93]	HRT-3 confocal microscopy	30	Eyes with slight xerophthalmia	Reconstruction of CSNP in images	NerveStitcher CNN	No validation or qualitative evaluation	N.A
2022	Setu et al. [88]	IVCM	197	Healthy and DED eyes	Segmentation of CNF and DC	U-Net, Mask R CNNs	Sens: 86.1%–94.4%, Spec: 90.1% Precis: 89.4%, ICC: 0.85–0.95	Cross validation
2022	Mou et al. [89]	HRT-3 confocal microscopy	300	CORN1500 dataset images	Grading of corneal nerve tortuosity	ImageNet, AuxNet	Acc: 85.64%	Cross validation
2021	Zéboulon et al. [95]	AS-OCT	607	Healthy and edematous corneas	Measurement of edema fraction	CNN	Threshold for diagnosis: 6.8%, AUC: 0.994, Acc: 98.7% Sens: 96.4%, Spec: 100%	Hold-out validation
2021	Deshmukh et al. [96]	ASP	504	Genetically confirmed GCD2 patients	Segmentation of cornea lesions	U-Net, CNN	IoU: 0.81 Acc: 99%	Cross validation
2021	Salahouddin et al. [138]	CCM	534	Healthy and type I diabetic eyes	DPN detection	U-net CNN	κ: 0.86, AUC: 0.86–0.95 Sens: 84%–92%, Spec: 71%–80%	Hold-out validation
2021	McCarron et al. [86]	HRT-3 confocal microscopy	73	Healthy and SIV-infected macaque eyes	Characterize difference in CSNP in acute SIV infection	deepNerve CNN	SIV infection reduced CNFL and fractal dimension (P = 0.01, P = 0.008)	N.A
2021	Yıldız et al. [139]	HRT-3 confocal microscopy	85	Healthy and chronic ocular surface pathology eyes	Segmentation of CSNP	GAN, U-Net CNN	PCC: 0.847–0.883 AUC: 0.8934–0.9439	N.A
2020	Scarpa et al. [85]	CCM	100	Healthy and DPN eyes	Classification of DPN and healthy eyes	CNN	Acc: 96%	Cross validation
2020	Williams et al. [84]	CCM	2137	Healthy and DPN eyes	Quantification of CSNP, detection of DPN	CNN	ICC: 0.656–0.933, AUC: 0.83 Spec: 87%, Sens: 68%	Hold-out validation
2020	Wei et al. [140]	HRT-3 confocal microscopy	139	Healthy eyes	Segmentation of CSNP	CNS-Net CNN	AUC: 0.96, Precis: 94% Sens: 96%, Spec: 75%	Hold-out validation

Acc = accuracy; ANFIS = adaptive neurofuzzy inference system; AS-OCT = anterior-segment optical coherence tomography; ASP = anterior-segment photography; AUC = area under curve; CCM = corneal confocal microscopy; CNF = corneal nerve fibers; CNFL = corneal nerve fiber length; CNN = convoluted neural networks; CSNP = corneal sub-basal nerve plexus; DC = dendritic cells; DED = dry eye disease; DPN = diabetic peripheral neuropathy; DSAEK = Descemet stripping automated endothelial keratoplasty; FECD = Fuchs endothelial corneal dystrophy; GDOCM = Gabor-domain optical coherence microscopy; GRBF = Gaussian radial basis function; HIS = hyperspectral imaging; ICC = interclass correlation coefficient; IoU = intersection over union; IVCM = in vivo confocal microscopy; κ = kappa index; N.A. = not available; PCC = Pearson’s correlation coefficient; PEE = punctate epithelial erosions; Precis = precision; RMSE = root mean square error; Sens = sensitivity; Spec = specificity; SVM = support vector machine