Elastic Deformation of Optical Coherence Tomography Images of Diabetic Macular Edema for Deep-Learning Models Training: How Far to Go?

Unlabelled: - Objective: To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME).

Methods: Prospective evaluation of OCT images of DME (n = 320) subject to elastic transformation, with the deformation intensity represented by ([Formula: see text]). Three sets of images, each comprising 100 pairs of scans (100 original & 100 modified), were grouped according to the range of ([Formula: see text]), including low-, medium- and high-degree of augmentation; ([Formula: see text] = 1-6), ([Formula: see text] = 7-12), and ([Formula: see text] = 13-18), respectively.

Cavity optofluidics: a μdroplet's whispering-gallery mode makes a μvortex.

We experimentally demonstrate light-flow interaction, in which the angular momentum of circulating light excites micro-vortices. In contrast with the solid-phase of matter, where one has to overcome static friction in order to start motion, liquids have no "static drag." Relevant to almost all optofluidic micro-systems hence, μWatt optical power is sufficient to start flows, even in liquids 50 times more viscous than water.