In Vivo Imaging of the Schlemm's Canal and the Response to Selective Laser Trabeculoplasty

In vivo identification of angle dysgenesis and its relation to genetic markers associated with glaucoma using artificial intelligence

PURPOSE

To predict the presence of angle dysgenesis on anterior-segment optical coherence tomography (ADoA) by using deep learning (DL) and to correlate ADoA with mutations in known glaucoma genes.

PARTICIPANTS

In total, 800 high-definition anterior-segment optical coherence tomography (AS-OCT) images were included, of which 340 images were used to build the machine learning (ML) model. Images used to build the ML model included 170 scans of primary congenital glaucoma (16 patients), juvenile-onset open-angle glaucoma (62 patients), and adult-onset primary open-angle glaucoma eyes (37 patients); the rest were controls (n = 85). The genetic validation dataset consisted of another 393 images of patients with known mutations that were compared with 320 images of healthy controls.

METHODS

ADoA was defined as the absence of Schlemm's canal, the presence of hyperreflectivity over the region of the trabecular meshwork, or a hyperreflective membrane. DL was used to classify a given AS-OCT image as either having angle dysgenesis or not. ADoA was then specifically looked for on AS-OCT images of patients with mutations in the known genes for glaucoma.

RESULTS

The final prediction, which was a consensus-based outcome from the three optimized DL models, had an accuracy of >95%, a specificity of >97%, and a sensitivity of >96% in detecting ADoA in the internal test dataset. Among the patients with known gene mutations, ( MYOC, CYP1B1, FOXC1, and LTBP2 ) ADoA was observed among all the patients in the majority of the images, compared to only 5% of the healthy controls.

CONCLUSION

ADoA can be objectively identified using models built with DL.

Iridocorneal angle imaging of a human donor eye by spectral-domain optical coherence tomography

Iridocorneal angle (ICA) details particularly the trabecular meshwork (TM), Schlemm's canal (SC), and collector channels (CCs) play crucial roles in the regulation of the aqueous outflow in the eyes and are closely associated with glaucoma. Current clinical gonioscopy imaging provides no depth information, and studies of 3D high-resolution optical coherence tomography (OCT) imaging of these structures are limited. We developed a custom-built spectral-domain (SD-) OCT imaging system to fully characterize the angle details. Imaging of a human cadaver eye reveals the visibility of details in the TM/SC/CC region via a 'crossline' scanning and a series of image processing. This shows that ICA imaging can be used for preoperative glaucoma inspections in the clinical setting with the proposed prototype.

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.