Feasibility of support vector machine learning in age‐related macular degeneration using small sample yielding sparse optical coherence tomography data. Issue 5 (6th March 2019)

• Record Type: Journal Article
• Authors: Quellec, Gwenolé; Kowal, Jens; Hasler, Pascal W.; Scholl, Hendrik P. N.; Zweifel, Sandrine; Konstantinos, Balaskas; de Carvalho, João Emanuel Ramos; Heeren, Tjebo; Egan, Catherine; Tufail, Adnan; Maloca, Peter M.
• Is Part Of: Acta ophthalmologica. Volume 97:Issue 5(2019)
• Page Start: e719
• Page End: e728
• Publication Date: 2019-03-06
• Subjects: artificial intelligence -- machine learning -- monitoring -- neovascular age‐related macular degeneration -- optical coherence tomography -- support vector machine; Ophthalmology -- Periodicals; 617.7005
• DOI: 10.1111/aos.14055
• Languages: English
• ISSNs: 1755-375X

Abstract

Abstract: Purpose: A retrospective pilot study is conducted to demonstrate the utility of a novel support vector machine learning (SVML) algorithm in a small three‐dimensional (3D) sample yielding sparse optical coherence tomography (spOCT) data for the automatic monitoring of neovascular (wet) age‐related macular degeneration (wAMD). Methods: From the anti‐vascular endothelial growth factor injection database, 588 consecutive pairs of OCT volumes (57.624 B‐scans) were selected in 70 randomly chosen wAMD patients treated with ranibizumab. The SVML algorithm was applied to 183 OCT volume pairs (17.934 B‐scans) in 30 patients. Four independent, diagnosis‐blinded retina specialists indicated whether wAMD activity was present between 100 pairs of consecutive OCT volumes (9800 B‐scans) in the remaining 40 patients for comparison with the SVML algorithm and a non‐complex baseline algorithm using only retinal thickness. The SVML algorithm was assessed using inter‐observer variability and receiver operating characteristic (ROC) analyses. Results: The retina specialists showed an average Cohen's κ of 0.57 ± 0.13 (minimum: 0.41, maximum: 0.83). The average κ between the proposed algorithm and the retina specialists was 0.62 ± 0.05 and 0.43 ± 0.14 between the baseline algorithm and the retina specialists. Using each of the four retina specialists as the reference, the proposed method showed a superior area under the ROC curve of 0.91 ± 0.03 compared to the ROC 0.81 ± 0.05 shown by the baseline algorithm. Conclusion: The SVML algorithm was as effective as the retina specialists were in detecting activity in wAMD. Support vector machine learning (SVML) may be a useful monitoring tool in wAMD suited for small samples that yield sparse OCT data possibly derived from self‐measuring OCT‐robots.