Latest Lumineyes News
Nov 20, 2011
An American medical doctor has developed a laser procedure that can &amp;ldquo;safely&amp;rdquo; turn brown eyes into blue. The cosmetic procedure which is touted to be relatively simple and &amp;ldquo;safe&amp;rdquo; has received quite a negative responsive from healthcare peers worldwide. Dr...
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Who are Lumineyes's competitors?
Competitors of Lumineyes include eSight, Affectiva, Blue Belt Technologies, Freedom Scientific, Ocusciences and 7 more.
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Mystic Pharmaceuticals develops devices that deliver drugs through the nose to the eyes.
eSight is a company that focuses on low-vision assistive technology within the healthcare industry. The company's main product is a pair of smart glasses that enhance the vision of individuals with significant central vision loss, using a high-speed camera and advanced algorithms to optimize and enhance real-time footage. The company primarily serves the healthcare sector, particularly eye care professionals and individuals with various eye conditions. It was founded in 2006 and is based in Toronto, Ontario.
Realistic Eye aims to provide a more natural appearance for the 750,000 patients in the US with an artificial eye that does not dilate and therefore appears unnatural. RealisticEye achieves these results by introducing a dilating artificial eye.
OcuSciences is developing a product to use images from the eye to detect diseases such as diabetes at an earlier stage.
Kjaya is a company that received a SBIR Phase I grant for a project entitled: Semi-Autonomous Adaptive Neural and Genetic Segmentation of Medical Images. Their Phase I project will implement a physician-assisted, real-time adaptive system for the segmentation of anatomical structures in 3D medical image data. Medical image segmentation seeks to change the representation of an anatomical structure, making it more easily analyzed. Because of the extreme variability of these structures in biological systems, current idiosyncratic manual methods currently in use are tedious, time consuming, and error prone. Image segmentation cannot in general be programmatically solved. The proposed system is a Neural Network (NN) based adaptation of the individual data using parallel Graphics Processing Units (GPUs) and coupled with a Genetic Algorithm (GA) based adaptation across GPU cores. The system will build a diagnostically useful segmentation of the anatomical feature within seconds from an area of interest outlined by a physician using a Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scan. Fast growth in medical imaging overwhelms available diagnosticians. An intuitive and inexpensive system to quickly and accurately deliver diagnostic relevant segmentation of medical images offers tremendous commercial value. Currently, each scan requires approximately 50 minutes of manual preparation. The diagnosis and treatment of an estimated 20 percent of diseases benefit from medical imaging. Newer scanning technologies have increased in resolution, but such techniques have not made segmenting easier or faster. The proposed method will enable more diagnostics to be done with the quality controlled directly by physicians.
Medical devices for the treatment of severe gastrointestinal disorders