Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.
Simrat K SodhiAustin PereiraJonathan D OakleyJohn GoldingCarmelina TrimboliDaniel B RussakoffNetan ChoudhryPublished in: PloS one (2022)
In isolation, total fluid volume most closely correlates with change in BCVA values between baseline and week 52. In combination with complimentary information from OCT-A, an improvement in the linear correlation score was observed. Average total retinal thickness provided a lower correlation, and thus provides a lower predictive outcome than alternative metrics assessed. Clinically, a machine-learning approach to analyzing fluid metrics in combination with lesion size may provide an advantage in personalizing therapy and predicting BCVA outcomes at week 52.
Keyphrases
- optical coherence tomography
- age related macular degeneration
- machine learning
- deep learning
- diabetic retinopathy
- end stage renal disease
- newly diagnosed
- optic nerve
- chronic kidney disease
- ejection fraction
- artificial intelligence
- high resolution
- peritoneal dialysis
- adipose tissue
- randomized controlled trial
- type diabetes
- mass spectrometry
- stem cells
- bone marrow
- big data
- insulin resistance
- social media
- study protocol
- health information
- smoking cessation