Prediction of Functional and Anatomic Progression in Lamellar Macular Holes

Abstract

Purpose: To use artificial intelligence to identify imaging biomarkers for anatomic and functional progression of lamellar macular hole (LMH) and elaborate a deep learning (DL) model based on OCT and OCT angiography (OCTA) for prediction of visual acuity (VA) loss in untreated LMHs.

Design: Multicentric retrospective observational study.

Participants: Patients aged >18 years 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 2 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-year follow-up. Biomarkers of anatomic 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 plexuses, choriocapillaris (CC), and 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 with 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 anatomic progression only.

Conclusions: Deep learning 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. The integrity of CC and SCP might play an important role in the progression of LMHs.

Financial disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.

Keywords: Biomarkers; Deep learning; Lamellar macular hole; OCT angiography; Progression.

Figure 1

Architecture of the DL model used. Two independent models based on Inception-Resnet-v2 architecture were trained with baseline OCT B-scan and OCTA acquisitions respectively. The OCT B-scan model was based on 5 foveal and perifoveal acquisitions, whereas the OCTA model combined in a multiple input architecture the en face acquisitions of SCP, ICP, DCP, and CC. The output of each model was then combined with a soft voting ensembling technique. With this technique, the degree of certainty of the 2 models for each outcome was combined to take the final decision. In example, if OCT B-scan based model predicted VA progression with a degree of certainty of 60%, and the OCTA model predicted VA stability with a degree of certainty of 70%, the final output was VA stability. CC = choriocapillaris; DCP = deep capillary plexus; DL= deep learning; ICP = intermediate capillary plexus; OCTA = OCT angiography; SCP = superficial capillary plexus; VA = visual acuity.

Figure 2

A, Baseline fovea crossing OCT B-scan acquisition of a LMH from the VA-PROG group. Parafoveal TL colocalizing with a zone of EZ disruption in the temporal fovea can be noted. B, GradCam visualization map highlighting the region that was fundamental to the software for classification in VA-PROG group. The hot area corresponds to critical OCT signs of VA progression according to the software. C, Follow-up acquisition demonstrating an anatomic progression of the TL and an increase in size of the EZ interruption accompanied by a VA loss of 6 ETDRS letters. BCVA = best-corrected visual acuity; EZ=ellipsoid zone; LMH= lamellar macular hole; TL = tissue loss; VA = visual acuity.

Figure 3

OCT angiogram of baseline SCP (A), DCP (B), and CC (C) with corresponding gradCAM maps (D–F) highlighting a zone of vascular rarefaction in a degenerative LMH experiencing VA loss during the follow-up. CC=choriocapillaris; DCP = deep capillary plexus; LMH = lamellar macular hole; SCP = superficial capillary plexus; VA = visual acuity.