Artificial intelligence-based fluid quantification predicts clinical outcomes in diabetic macular oedema eyes treated with intravitreal dexamethasone implants: DIADEMA project

Abstract

Aim To explore associations between artificial intelligence (AI)-based baseline optical coherence tomography (OCT) fluid compartment quantifications and 12-month visual outcomes in diabetic macular oedema (DME) eyes treated with the intravitreal dexamethasone implant. Methods This was a multicentre, real-world, national DME database and associated OCT dataset study. Demographics, visual acuity (VA), treatments and visit data were collected using a validated web-based tool (Fight Retinal Blindness). Fluid compartment quantifications, including intraretinal fluid (IRF) and subretinal fluid (SRF), were measured in nanolitres (nL) using a validated AI tool (Discovery). Univariate and multivariate regression mixed models evaluated associations between anatomical variables and VA outcomes. Results A total of 101 treatment-naïve DME eyes were grouped into quartiles according to their fluid volume for each fluid compartment (Q1: lowest volume, Q4: highest volume). Baseline IRF was associated with greater VA gains at month 12 (+6.34 letters, p=0.07) but poorer final VA (-8.95, p=0.07), while SRF was associated with worse final VA at 12 months (-12.5, p=0.01). At month 3, IRF was associated with a VA decrease at 12 months (-13.7, p=0.02) and lower final VA (-29.8, p<0.001). At month 12, IRF was associated with lower final VA (-11.6, p=0.03). Quantitatively, a reduction of 100 nL of IRF at 3 months was associated with a +1.54 letters gain (p=0.03) in the multivariate analysis. Conclusion This real-world, multicentre study describes objective baseline fluid volumes that predict visual outcomes at 12 months in routine clinical care. Accurate quantification of baseline fluid volumes may play a predictive role for final visual outcomes.