Everything real about unreal artificial intelligence in diabetic retinopathy and in ocular pathologies

Table 3 Summary of studies using artificial intelligence to detect progression in Glaucomatous eyes

Ref.	No. of eyes	Instrument	Approach	Comments
Lin et al[63]	80	SAP	Supervised ML	Sensitivity-86%; Specificity-88%
Goldbaum et al[64]	478 suspects; 150 glaucoma; 55 stable glaucoma	SAP	Unsupervised ML	Specificity-98.4%, AROC not available; Use of variational Byesian. Independent component analysis mixture model in indentifying patterns of glaucomatous visual field defects and its validation
Wang et al[65]	11817 (method developing cohort) and 397 (clinical evaluation cohort)	SAP	Unsupervised ML	AROC of the archetype method 0.77
Yousefi et al[16]	939 Abnormal SAP and 1146 normal SAP in the cross section and 270 glaucoma in the longitudinal database	SAP	Unsupervised ML	Sensitivity 34.5%-63.4% at specificity 87% Comment: it took 3.5 years for ML analysis to detect progression while it took over 3.5 years for other methods to detect progression in 25% of eyes
Belghith et al	27- progressing; 26-stable glaucoma and 40 healthy controls		SD OCT Supervised ML	Sensitivity -78% Specificity in normal eyes-93%; 94% in non-progressive eyes

ML: Machine learning; OCT: Optical coherence tomography.