Deep Learning–Based Choroidal Boundary Detection in Geographic Atrophy Using Spectral-Domain Optical Coherence Tomography

TL;DR

A deep learning model helps detect choroidal boundaries in eyes with geographic atrophy using OCT scans, significantly reducing manual work but requiring human verification for accuracy.

Contribution

A deep learning model for choroidal boundary detection in GA is evaluated, showing high efficiency and accuracy with AI-assisted manual verification.

Findings

The model achieved 94.8% accuracy for inner choroidal boundary detection with high precision.

Outer choroidal boundary detection had higher error rates, but 94.2% were acceptable with minor deviations.

AI-assisted verification reduced processing time by 90% compared to manual segmentation alone.

Abstract

Background/Objectives: To evaluate the challenges and limitations of a deep learning model for automated choroidal boundary detection in eyes with geographic atrophy (GA) using spectral-domain OCT (SD-OCT), and to assess the workflow efficiency of an AI-assisted manual verification approach. Methods: In this retrospective study, total 5723 scans (Heidelberg Spectralis) with GA were analyzed. A previously validated tool (NMI ChoroidAI) was used to segment the choroidal inner (CIB) and outer (COB) boundaries. We compared the “AI-assisted” workflow (automated segmentation followed by manual verification) against “manual segmentation only” in terms of accuracy and time consumption. Slice-wise boundary errors were graded as 0 (accurate), 1 (≤33% deviation), 2 (33–66% deviation), or 3 (>66% deviation). Outcomes included error rates and weighted F1 score (and precision where applicable). Total time for manual-only segmentation versus AI-assisted verification was recorded. -Interreader variability was assessed between the two readers using intraclass correlation coefficient. Results: For CIB, only 5.2% of B-scans showed any deviation (strictly accurate in 94.8%), with weighted F1 score 0.97 and precision 1.00. COB was more error-prone: 19.0% of B-scans showed deviation; however, when minor deviations were considered acceptable, COB acceptability increased to 94.2% (i.e., 5.8% remained >33% deviated). Only 13.2% of B-scans required minor manual correction. For a 97-scan volume, processing time decreased from an average of 7 h (manual only) to 45 min (AI + human verification), an approximate 90% reduction in manual effort. Inter-reader agreement was high (ICC 0.923 for CIB and 0.938 for COB). Conclusions: Although the deep learning model exhibits limitations in COB detection due to artifacts, it serves as a valuable assistive tool. Our model substantially reduces human effort, but mandatory human verification is required to correct boundary errors caused by hyper-transmission before use in clinical trials.

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