Prediction of functional and anatomical progression in lamellar macular holes

Purpose To use artificial intelligence to identify imaging biomarkers for anatomical and functional progression of lamellar macular hole (LMH) and to elaborate a deep learning (DL) model based on optical coherence tomography (OCT) and OCT angiography (OCTA) for prediction of visual acuity (VA) loss in untreated LMHs. Design Multicentric retrospective observational study Participants Patients >18 years old diagnosed with idiopathic LMHs with availability of good quality OCT and OCTA acquisitions at baseline and a follow up>2 years were recruited. Methods A DL model based on soft voting of two separate models (OCT and OCTA-based respectively) was trained for identification of cases with VA loss> 5 ETDRS letters (attributable to LMH progression only) during a 2-years follow-up. Biomarkers of anatomical and functional progression of LMH were evaluated with regression analysis, feature learning(support vector machine(SVM) model) and visualization maps. Main Outcome measures Ellipsoid zone(EZ) damage, volumetric tissue loss (TL), vitreopapillary adhesion (VPA), epiretinal proliferation, central macular thickness (CMT), parafoveal vessel density (VD) and vessel length density (VLD) of retinal capillary plexa, choriocapillaris (CC) flow deficit density (FDD). Results Functionally progressing LMHs (VA-PROG group, 41/139 eyes (29.5%)) showed higher prevalence of EZ damage, higher volumetric TL, higher prevalence of VPA, lower superficial capillary plexus(SCP) VD and VLD and higher CC FDD compared to functionally stable LMHs (VA-STABLE group,98/139 eyes (70.5%)). The DL and SVM models showed 92.5% and 90.5% accuracy respectively. The best performing features in the SVM were EZ damage, TL, CC FDD and parafoveal SCP VD. Epiretinal proliferation and lower CMT were risk factors for anatomical progression only. Conclusions DL can accurately predict functional progression of untreated LMHs over 2 years. The use of AI might improve our understanding of the natural course of retinal diseases. Integrity of CC and SCP might play an important role in the progression of LMHs.