Artificial Intelligence Driven Adjusted OCT-Based Classification System for Diabetic Macular Edema (AIDME)

To develop and validate an adjusted scoring-based classification system for Diabetic Macular Edema (DME) using quantitative and qualitative parameters. The system aims to improve clinical decision making and correlate disease severity with visual outcomes.

A cohort of patients with DME was analyzed using optical coherence tomography (OCT) data. Key quantitative variables included retinal thickness (RT), intraretinal fluid (IRF), and subretinal fluid (SRF), which were scored according to severity thresholds (0-2 points per parameter). Qualitative biomarkers, including disorganization of the retinal inner layers (DRIL) and epiretinal membrane (ERM), were incorporated as severity modifiers (+2 for DRIL and +1 for ERM). The patients were classified based on the total score as follows: Mild (0-3 points), Moderate (4-6 points), and severe (>7 points). Visual acuity (logMAR) was analyzed to validate the system and to assess its correlation with disease severity. Statistical analyses included descriptive summaries and ANOVA for intergroup comparisons.

A total of 71 patients were classified into three groups: mild (48 patients, mean logMAR 0.45), moderate (18 patients, mean logMAR 0.56), and severe (5 patients, mean logMAR 0.92). The system showed significant differences in logMAR values (p = 0.014). ROC curve analysis showed an area under the curve (AUC) of 0.89. This adjusted scoring method aligned well with the clinical expectations, emphasizing the impact of fluid accumulation and structural biomarkers on vision impairment.

The adjusted scoring-based classification system for DME demonstrated a robust correlation between the disease severity and visual acuity. Integrating quantitative measures and qualitative markers offers a practical and clinically relevant approach for stratifying DME severity. This method has the potential to enhance decision making in routine practice and research applications.