Comparative study of deep learning and standard clinical assessment methods for early glaucoma diagnosis

Abstract

Glaucoma is the leading cause of irreversible blindness globally, and its early detection remains a persistent challenge across both high-income and resource-constrained health systems. Standard clinical assessment encompassing Goldmann applanation tonometry, standard automated perimetry, optical coherence tomography, slit-lamp biomicroscopy, and gonioscopy constitutes the established diagnostic reference standard but is burdened by inter-observer variability, test-retest measurement fluctuation, patient-dependent performance, and an inherent structural inability to detect pathological change prior to significant functional loss. Deep learning algorithms, and in particular convolutional neural networks and vision transformer architectures applied to colour fundus photography and optical coherence tomography imaging, have advanced substantially in diagnostic accuracy and now achieve performance metrics that are comparable to or, in many documented instances, superior to those of trained human clinicians on equivalent imaging tasks. This paper presented a structured comparative analysis of deep learning and standard clinical assessment approaches for early glaucoma diagnosis. It examined the diagnostic performance, practical capabilities and limitations, and the clinical contexts in which each approach is most appropriately deployed. The analysis concluded that deep learning systems demonstrated compelling diagnostic utility that is well-evidenced in the peer-reviewed literature, and that their integration within hybrid diagnostic workflows in which automated image analysis augments rather than replaces specialist clinical judgement represents the most clinically defensible and practically effective pathway to improving early glaucoma detection at both individual and population levels.