Associate Professor At Mathematics And Computer Science Department
Capsule endoscopy is a procedure used to record images of the gastrointestinal tract for use in medical diagnosis. This procedure involves the ingestion of a small capsule, similar in shape to a standard pharmaceutical pill, that contains a tiny camera and an array of LEDs powered by a battery. The capsule takes a number of images per second, which are wirelessly transmitted to an array of receivers connected to a portable recording device carried by the patient. The use of video capsule endoscopy in the colon has been proposed as an alternative colorectal cancer-screening test.
The main objective of this applied research project has been to develop a scalable AI solution to significantly speed up the process of inspection of the gastrointestinal tract. By using Deep Learning techniques, a computer can inspect hundreds of thousands of images and select the ones with anomalies, just in a few minutes instead of a few hours.
The technological breakthrough of this solution is the reduction in time and costs of analysis for the diagnosis, as no specialized professionals or proper facilities are needed. At the same time, the system works at human accuracy levels.
Colorectal cancer screening is nowadays costly, invasive and labour-intensive, and deemed an unsuitable population-wide index screening tool. This AI system democratizes access to screening and its inclusion in 5G remote medical examination services will help reduce the burden of residents living in remote areas, as well as of elderly people who must go to specialized facilities for medical attention.
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