Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

Advancements in high-resolution imaging of the iridocorneal angle

High-resolution imaging methods of the iridocorneal angle (ICA) will lead to enhanced understanding of aqueous humor outflow mechanisms and a characterization of the trabecular meshwork (TM) morphology at the cellular level will help to better understand glaucoma mechanics (e.g., cellular level biomechanics of the particulate glaucomas). This information will translate into immense clinical value, leading to more informed and customized treatment selection, and improved monitoring of procedural interventions that lower intraocular pressure (IOP). Given ICA anatomy, imaging modalities that yield intrinsic optical sectioning or 3D imaging capability will be useful to aid in the visualization of TM layers. This minireview examines advancements in imaging the ICA in high-resolution.

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

Moving beyond the Slit-Lamp Gonioscopy: Challenges and Future Opportunities

After almost a century from its introduction in clinical practice, slit-lamp gonioscopy is still considered the reference standard for evaluating the anterior chamber angle (ACA). Gonioscopy is essential for diagnosing angle closure disease, and ACA features are included in glaucoma’s diagnostics and treatments algorithms. However, shortcomings of slit-lamp gonioscopy include a steep learning curve, lack of agreement between examiners and poor documentation. Thanks to advances in miniaturization and computing, new instruments for digital gonioscopy have been developed and marketed. This narrative review focuses on the Gonioscope GS-1, which permits semi-automated circumferential documentation of the ACA in real-colour photographs. Advantages and disadvantages of GS-1 compared with slit-lamp gonioscopy and other ACA imaging technologies such as optical coherence tomography are discussed. Finally, potential opportunities offered by this device for telemedicine, virtual clinics, and automatic classification with deep learning are presented.