Hyperspectral (HS) imaging presents itself as a non-contact, non-ionizing and non-invasive technique, proven to be suitable for medical diagnosis. However, the volume of information contained in these images makes difficult providing the surgeon with information about the boundaries in real-time. To that end, High-Performance-Computing (HPC) platforms become necessary. This paper presents a comparison between the performances provided by five different HPC platforms while processing a spatial-spectral approach to classify HS images, assessing their main benefits and drawbacks.
Glaucoma is the leading cause of irreversible blindness in the world, and primary angle closure glaucoma (PACG) is one of the main subtypes. PACG patients have narrow chamber angle and can be diagnosed by goniscopy, which may cause discomfort and relies too much on personal experience. Anterior segment OCT is able to provide 3D scan of the anterior chamber and assist the ophthalmologists evaluate the condition of chamber angle. It’s faster and objective compare with goniscopy.
Welcome to the Retinal Fundus Glaucoma Challenge! REFUGE was organized as a half day Challenge in conjunction with the 5th MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), a Satellite Event of the MICCAI 2018 conference in Granada, Spain. The goal of the challenge is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images. With this challenge, we made available a large dataset of 1200 annotated retinal fundus images.
Effects of plants on glucose metabolism have been highlighted and are of particular interest in Type 2 Diabetes (T2DM). Zingiber Officinale (ZO)for instance, is a plant widely used as a spice, known for its hypoglycaemic and hypolipidaemic virtues. However, few studies were conducted in humans, and none have used reference methods for assessing glucose metabolism. The study aims to evaluate the effect of ginger extracts supplementation on diabetes mellitus patients metabolic profile.
Recent researches suggest assessing more ethnic disparities in hypertension (HTN). On the other hand, studies reveal impressive mortality rates due to cardiovascular diseases for some breeds compared to others.
In order to study the application of machine learning in myoelectric data, the machine learning method has been used for data mining and analysis so as to find correlation characteristics. More than 2,300 myoelectric examination data from Sichuan Provincial Hospital of Traditional Chinese Medicine (TCM) for 10 months has been collected and recorded. By means of setting the inclusion criteria and excluding the irrelevant factors, the facial nerve electromyography and auditory brainstem response test reports that meet the research criteria have been screened out.
The pressure sensors are represented by black circles, which are located in the three zones of each foot. For the left foot: S1 and S2 cover the forefoot area. S3, S4, and S5 the midfoot area. S6 and S7 the rearfoot or heel area. Similarly, for the right foot: S8 and S9 represent the forefoot area. S10, S11, S12 the midfoot area. S13 and S14 the heel area. The values of each sensor are read by the analog inputs of an Arduino mega 2560.
