Standardized OCT classification, grading system needed for diabetic macular edema
Click Here to Manage Email Alerts
Key takeaways:
- Knowledge gaps and controversies remain a barrier to understanding the relevance of OCT biomarkers as predictors for DME.
- Home-based OCT used with artificial intelligence may increase compliance.
Despite advances in research on OCT biomarkers of diabetic macular edema, a standardized classification and grading system is still necessary to fill knowledge gaps, according to a recently published review.
“Research on OCT features in DME has expanded at unprecedented pace in the past decade,” Simon KH Szeto, from the department of ophthalmology and visual sciences at the Chinese University of Hong Kong, and colleagues wrote in Progress in Retinal and Eye Research. “This has opened the window of opportunity for researchers and clinicians alike to better understand the complex pathophysiology of DME.”
According to researchers, DME can be classified on OCT as non-center-involved edema (non-CI-DME) and center-involved edema (CI-DME), while no DME is defined as lack of retinal thickening or hard exudates in the macula. These classifications, which are supported by evidence from the Diabetic Retinopathy Clinical Research Network, can be used to guide treatment decisions. With only 14% of eyes with non-CI-DME progressing to CI-DME at 1 year, non-CI-DME eyes with good visual acuity can initially be observed.
However, recent research has noted potential pitfalls of these standards, including that subclinical progressive retinal dysfunction may precede a drop in visual acuity and long-term visual consequences remain unknown.
Szeto and colleagues also wrote that home-based OCT may improve compliance and reduce re-injections by lowering cost and maximizing convenience to patients, while also lowering the burden on medical staff, especially when combined with an AI-based diagnostic algorithm.
“Despite the improved knowledge on the significance of OCT features in DME, most of the evidence are derived from retrospective studies or secondary analysis of clinical trials,” Szeto and colleagues wrote. “There are knowledge gaps and controversies that need to be addressed in prospective studies. For example, there are conflicting reports on the relevance of OCT biomarkers as predictors of treatment response of DME to various therapeutic agents. The application of AI may help define novel OCT biomarkers and new DME phenotypes that may inform personalized treatment decisions.”
Collapse
Read more about
Click Here to Manage Email Alerts