What’s cool in glaucoma

What’s cool in glaucoma

The six speakers in the Hot Topics symposium at the recently held 5th

Annual Asia-Pacific Glaucoma Congress continue to push the envelope in glaucoma imaging, diagnosis, surgery, and genetics.

We enjoyed hearing about their exploits and how the field was progressing … Here’s the inside scoop on what the cool kids are talking about! Detecting RNFL defects Kicking off the session,

Dr. Christopher Leung from the Chinese University of Hong Kong,

illustrated how retinal nerve fiber layer (RNFL) optical texture analysis (ROTA) integrated RNFL thickness ลาวสามัคคี

and reflectance to enhance the optical texture and trajectories of individual axonal fiber bundles.

As such, ROTA can detect RNFL defects, which go usually undetected

by conventional optical coherence tomography (OCT) imaging or analysis of RNFL/ganglion

cell-inner plexiform layer thickness, and provide better clarity on structure-function disagreements.

 “The structure-function discordance in early glaucoma can be attributed to the fact that test locations of the standard 24-2 visual field (VF)

 test may not be sufficient to cover focal RNFL defects, particularly when they are located at the macula,”

Dr. Leung noted. Though the 10-2 and 24-2 test strategies, combined, achieve better structurefunction agreement,

 they can be tiring for patients. Instead, the relatively new 24-2C VF test includes 10 additional test locations over the central 10 degrees to better capture central VF loss.

OCT for improved diagnostic performance Next, Dr. Leopold Schmetterer from the Singapore Eye Research Institute,

 demonstrated how anatomical correction of OCT images improved their diagnostic performance, particularly in early glaucoma.

Indeed, many reclassifications based on the corrections are due to anatomical particularities rather than RNFL thinning. Although machine learning algorithms could majorly revolutionize glaucoma care,

Dr. Schmetterer urges critical analysis of studies that employ these methods.

“Ask questions about age-, ethnicity- and gender-matching between control and glaucoma cases.

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