Artificial intelligence, machine learning and data are at the heart of the digital revolution we are currently experiencing. To support business decision-making, one must go beyond predictions and employ causal data science, which enables you to identify the factors that most directly help influence customer decision-making. We explain it in this video!
The endoscopic capsule is a procedure used to record images of the gastrointestinal tract for use in the medical diagnosis. This procedure involves the ingestion of a small capsule, which takes several images per second, transmitted with wireless technology to a series of receivers connected to a portable recording device that the patient carries. In order to be able to emit an agile diagnosis of the large volume of images captured, the application of artificial intelligence techniques is required that will allow obtaining results in minutes and not hours as it has been going on.
Develop a scalable AI solution to significantly accelerate the process of inspection of the gastrointestinal tract. Through the use of deep learning techniques, a computer can inspect hundreds of thousands of images and select those that have anomalies, only in a few minutes instead of a few hours.
The technological advance of this solution is the reduction of the time and the costs of analysis for the diagnosis, since specialized professionals are not needed nor suitable facilities. At the same time, the system works at levels of human accuracy. Colorectal cancer screening is now costly, invasive, and labor-intensive, and is considered an index detection tool that is not suitable for the population. This AI system democratizes access to projection and its inclusion in remote medical examination services 5G will help reduce the burden of residents in remote areas as well as elderly people who have to go to specialized centers of medical attention.
DataScience@UB designs and prototype a machine that includes an intelligent vision system based on semantic segmentation through convolutional neural networks to automate the classification of skins and leather.
We have won a new competition with the Cartographic and Geological Institute of Catalonia, to apply deep learning techniques in the detection of changes in the network of infrastructures and constructions in the territory with the aim of automating the process of elaboration of the Topographic map of Catalonia.