Automated measurement of macular neovascularization lesion size in nAMD using AI segmentation Article Swipe
Anna Vahldiek
,
Lukas Heine
,
Benja Vahldiek
,
Jens Schröter
,
J. Wolf
,
Laurenz Pauleikhoff
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s00417-025-07007-0
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s00417-025-07007-0
Purpose To compare artificial intelligence (AI)-based annotations of hyperreflective material (HRM) and manual demarcation of macular neovascularization (MNV) on optical coherence tomography (OCT) volume scans in neovascular age-related macular degeneration (nAMD), and to assess the suitability of AI-driven OCT segmentation for longitudinal lesion monitoring. Methods In this retrospective study, 42 eyes from 36 patients (21 f, 15 m; mean age baseline 76.6 y) with exudative nAMD were analyzed using longitudinal spectral-domain OCT data. Manual MNV demarcations on en-face OCT projections served as ground truth and were compared to AI-predicted HRM segmentations generated by a 3D nU-Net model on OCT scans. HRM and MNV lesion areas were quantified at multiple time points, and agreement between manual and AI-based measurements was evaluated using Pearson correlation, ordinary least squares regression and robust regression. Results A highly similar mean lesion growth was observed when comparing HRM/MNV lesion sizes in longitudinal measurements. Point-by-point comparison revealed a strong overall correlation (r = 0.78) between AI-predicted and manually annotated HRM areas with increasing significance with longer follow-up. However, two aspects were responsible for some AI measurements being larger than manual measurements: At baseline, AI measurements included hyperreflective subretinal fluid as HRM, which was resorbed after three anti-VEGF injections, and during longer-term follow-up, manually annotated MNV areas were occasionally smaller than those derived from AI-based HRM segmentation due to the manual underestimation of very thin HRM. Conclusions AI-based segmentation of HRM on OCT scans demonstrates strong overall agreement with manual MNV measurements, particularly on longitudinal assessments. Despite some AI-based overestimations occurring at baseline and some manual MNV underestimations during follow-up, measurements between both methods were highly comparable over time.
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Automated measurement of macular neovascularization lesion size in nAMD using AI segmentationWork title
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2025Year of publication
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2025-11-17Full publication date if available
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Anna Vahldiek, Lukas Heine, Benja Vahldiek, Jens Schröter, J. Wolf, Laurenz PauleikhoffList of authors in order
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https://doi.org/10.1007/s00417-025-07007-0Publisher landing page
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| abstract_inverted_index.intelligence | 5 |
| abstract_inverted_index.longitudinal | 42, 70, 146, 247 |
| abstract_inverted_index.measurements | 118, 179, 188, 263 |
| abstract_inverted_index.occasionally | 211 |
| abstract_inverted_index.particularly | 245 |
| abstract_inverted_index.segmentation | 40, 219, 231 |
| abstract_inverted_index.significance | 167 |
| abstract_inverted_index.measurements, | 244 |
| abstract_inverted_index.measurements. | 147 |
| abstract_inverted_index.measurements: | 184 |
| abstract_inverted_index.retrospective | 48 |
| abstract_inverted_index.segmentations | 91 |
| abstract_inverted_index.Point-by-point | 148 |
| abstract_inverted_index.hyperreflective | 9, 190 |
| abstract_inverted_index.overestimations | 252 |
| abstract_inverted_index.spectral-domain | 71 |
| abstract_inverted_index.underestimation | 224 |
| abstract_inverted_index.underestimations | 260 |
| abstract_inverted_index.neovascularization | 17 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5119007152 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |