Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD

• Published in: Eye (London) 2023-12, Vol.37 (18), p.3793-3800
• Main Authors: Pawloff, Maximilian, Gerendas, Bianca S., Deak, Gabor, Bogunovic, Hrvoje, Gruber, Anastasiia, Schmidt-Erfurth, Ursula
• Format: Article
• Language: English
• Subjects: 692/699/3161/3175; 692/700/1421; Age; Angiogenesis Inhibitors - therapeutic use; Automation; Deep Learning; Humans; Intravitreal Injections; Laboratory Medicine; Macular degeneration; Medicine; Medicine & Public Health; Ophthalmology; Pharmaceutical Sciences/Technology; Reproducibility of Results; Retina; Subretinal Fluid - diagnostic imaging; Surgery; Surgical Oncology; Tomography, Optical Coherence - methods; Vascular endothelial growth factor; Vascular Endothelial Growth Factor A; Visual Acuity; Wet Macular Degeneration - diagnosis; Wet Macular Degeneration - drug therapy
• ISSN: 0950-222X; 1476-5454
• DOI: 10.1038/s41433-023-02615-8

Purpose To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated measurements with central retinal subfield thickness (CSFT) and fluid volume values. Methods We utilized an automated deep learning approach to quantify macular fluid in SD-OCT volumes (Cirrus, Spectralis, Topcon) from patients of HAWK and HARRIER Studies. Three-dimensional volumes for IRF and SRF were measured at baseline and under therapy in the central millimeter and compared to fluid gradings, CSFT and foveal centerpoint thickness (CPT) values measured by the Vienna Reading Center. Results 41.906 SD-OCT volume scans were included into the analysis. Concordance between human expert grading and automated algorithm performance reached AUC values of 0.93/0.85 for IRF and 0.87 for SRF in HARRIER/HAWK in the central millimeter. IRF volumes showed a moderate correlation with CSFT at baseline (HAWK: r = 0.54; HARRIER: r = 0.62) and weaker correlation under therapy (HAWK: r = 0.44; HARRIER: r = 0.34). SRF and CSFT correlations were low at baseline (HAWK: r = 0.29; HARRIER: r = 0.22) and under therapy (HAWK: r = 0.38; HARRIER: r = 0.45). The residual standard error (IRF: 75.90 µm; SRF: 95.26 µm) and marginal residual standard deviations (IRF: 46.35 µm; SRF: 44.19 µm) of fluid volume were high compared to the range of CSFT values. Conclusion Deep learning-based segmentation of retinal fluid performs reliably on OCT images. CSFT values are weak indicators for fluid activity in nAMD. Automated quantification of fluid types, highlight the potential of deep learning-based approaches to objectively monitor anti-VEGF therapy.