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.

Familial retinal arteriolar tortuosity and quantification of vascular tortuosity using swept-source optical coherence tomography angiography.

Purpose: Familial retinal arteriolar tortuosity (FRAT) is a rare autosomal dominant disorder that is characterized by tortuosity of the second and higher order retinal arterioles. We implement swept-source optical coherence tomography angiography (SS-OCTA) to quantify vessel tortuosity in patients with FRAT. We hypothesize that patients with FRAT will have higher retinal arteriole tortuosity when compared to controls.

Deep-learning based, automated segmentation of macular edema in optical coherence tomography.

Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions.