Deep learning-based correction of cataract-induced influence on macular pigment optical density measurement by autofluorescence spectroscopy.
Akira ObanaKibo OteYuko GohtoHidenao YamadaFumio HashimotoShigetoshi OkazakiRyo AsaokaPublished in: PloS one (2024)
The usefulness of the DL correction method was validated. Deep learning reduced the error for a relatively good autofluorescence image quality. Poor-quality images were not corrected.
Keyphrases
- deep learning
- image quality
- convolutional neural network
- high resolution
- artificial intelligence
- computed tomography
- optical coherence tomography
- machine learning
- high glucose
- cataract surgery
- diabetic rats
- diabetic retinopathy
- dual energy
- single molecule
- high speed
- drug induced
- oxidative stress
- magnetic resonance imaging
- endothelial cells
- quality improvement