Abstract
Differentiating the two main forms of primary glaucoma (open-angle and closed-angle glaucoma) depends on the correct assessment of the anterior chamber angle (ACA). This assessment will determine the management plan and prognosis for the disease. The standard method of examining the angle has been, for many years, slit-lamp gonioscopy. This method, although clinically still useful, is less robust for patient follow up and clinical research, given its low reproducibility. Several imaging technologies have been developed in recent years to improve the evaluation of the ACA and overcome the shortcomings of gonioscopy. These recent advances include three-dimensional and 360° analysis by Swept-Source OCT (SS-OCT, CASIA, Tomey, Nagoya, Japan), the introduction of deep learning algorithms for automatic imaging classification and new goniophotographic systems. SS-OCT allows for the first time the assessment of the circumferential extension of angle closure with moderate to good diagnostic performance compared with gonioscopy. Deep learning algorithms are showing promising results for the automation of imaging analysis, and may potentially save physicians' time in regards of the interpretation of the images. Lastly, goniophotograph systems have the distinct advantage of recordability of gonioscopic findings and are most closely matched to the findings of slit-lamp gonioscopy.