Generating retinal flow maps from structural optical coherence tomography with artificial intelligence.

Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures retinal blood flow, to train an AI algorithm to generate flow maps from standard optical coherence tomography (OCT) images, exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer flow from single structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.

Forecasting future Humphrey Visual Fields using deep learning.

Purpose: To determine if deep learning networks could be trained to forecast future 24-2 Humphrey Visual Fields (HVFs).

Methods: All data points from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a university database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction.

Diminished orientation-specific surround suppression of visual processing in schizophrenia.

Visual perception of a stimulus is a function of the visual context in which it is displayed. Surround suppression is a specific form of contextual modulation whereby the perceived contrast of a center stimulus is decreased by a high-contrast surround. Recent studies have demonstrated that individuals with schizophrenia are less prone to visual contextual effects, suggesting impairments in cortical lateral connectivity.