Feasibility of support vector machine learning in age-related macular degeneration using small sample yielding sparse optical coherence tomography data.
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.
['Quellec|Gwenolé|G|', 'Kowal|Jens|J|', 'Hasler|Pascal W|PW|', 'Scholl|Hendrik P N|HPN|', 'Zweifel|Sandrine|S|', 'Konstantinos|Balaskas|B|', 'de Carvalho|João Emanuel Ramos|JER|', 'Heeren|Tjebo|T|', 'Egan|Catherine|C|', 'Tufail|Adnan|A|', 'Maloca|Peter M|PM|https://orcid.org/0000-0002-4794-5859']
D000368:Aged D000369:Aged, 80 and over D000465:Algorithms D018450:Disease Progression D005240:Feasibility Studies D005260:Female D005500:Follow-Up Studies D006801:Humans D021621:Imaging, Three-Dimensional D008266:Macula Lutea D008297:Male D008875:Middle Aged D010865:Pilot Projects D011379:Prognosis D012372:ROC Curve D012189:Retrospective Studies D060388:Support Vector Machine D013997:Time Factors D041623:Tomography, Optical Coherence D057135:Wet Macular Degeneration
support vector machine machine learning optical coherence tomography monitoring artificial intelligence neovascular age-related macular degeneration