AIMC Topic: Eye
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Deep learning-based detection of hepatobiliary disorders in ophthalmic imaging.
The Lancet. Digital health
Feb 1, 2021
Artificial intelligence in the ophthalmic landscape.
Nepalese journal of ophthalmology : a biannual peer-reviewed academic journal of the Nepal Ophthalmic Society : NEPJOPH
Jan 1, 2020
Diagnostic Quality Assessment of Ocular Fundus Photographs: Efficacy of Structure-Preserving ScatNet Features.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Various ophthalmic procedures critically depend on high-quality images. For instance, efficiency of teleophthalmology, a framework to bring advanced eye care to remote regions, is determined by the capability of assessing diagnostic quality of ocular...
High Intraocular Pressure Detection from Frontal Eye Images: A Machine Learning Based Approach.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
This paper presents a novel framework to detect the status of intraocular pressure (normal/high) using solely frontal eye image analysis. The framework is based on machine learning approaches to extract six features from frontal eye images. These fea...
Robot-assisted retinal vein cannulation with force-based puncture detection: Micron vs. the steady-hand eye robot.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Aug 1, 2016
Retinal vein cannulation is a demanding procedure where therapeutic agents are injected into occluded retina veins. The feasibility of this treatment is limited due to challenges in identifying the moment of venous puncture, achieving cannulation and...
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