Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add more filters










Database
Language
Publication year range
1.
IEEE J Transl Eng Health Med ; 11: 487-494, 2023.
Article in English | MEDLINE | ID: mdl-37817823

ABSTRACT

- 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. Three retina specialists evaluated all datasets in a blinded manner and designated each image as 'original' versus 'modified'. The rate of assignment of 'original' value to modified images (false-negative) was determined for each grader in each dataset. RESULTS: The false-negative rates ranged between 71-77% for the low-, 63-76% for the medium-, and 50-75% for the high-augmentation categories. The corresponding rates of correct identification of original images ranged between 75-85% ([Formula: see text]0.05) in the low-, 73-85% ([Formula: see text]0.05 for graders 1 & 2, p = 0.01 for grader 3) in the medium-, and 81-91% ([Formula: see text]) in the high-augmentation categories. In the subcategory ([Formula: see text] = 7-9) the false-negative rates were 93-83%, whereas the rates of correctly identifying original images ranged between 89-99% ([Formula: see text]0.05 for all graders). CONCLUSIONS: Deformation of low-medium intensity ([Formula: see text] = 1-9) may be applied without compromising OCT image representativeness in DME. Clinical and Translational Impact Statement-Elastic deformation may efficiently augment the size, robustness, and diversity of training datasets without altering their clinical value, enhancing the development of high-accuracy algorithms for automated interpretation of OCT images.


Subject(s)
Deep Learning , Diabetes Mellitus , Diabetic Retinopathy , Macular Edema , Humans , Macular Edema/diagnostic imaging , Diabetic Retinopathy/diagnosis , Tomography, Optical Coherence/methods , Retina
2.
Opt Express ; 26(15): 19115-19122, 2018 Jul 23.
Article in English | MEDLINE | ID: mdl-30114171

ABSTRACT

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. We map the flows to be three-dimensional (3D) by using a technique based on fluorescent nano-emitters; to reveal, as expected, flow speeds proportional to power divided by viscosity.

SELECTION OF CITATIONS
SEARCH DETAIL
...