R&D Projects

DAPLIA

Application of artificial intelligence for the diagnosis of lumbar pathologies

Back pain is considered the most common musculoskeletal condition in the adult population, with a prevalence of around 84%, i.e. five out of six people will experience back pain in adulthood. Despite its high morbidity levels, only 15% of patients with back pain are diagnosed with a specific type of low back pain, a fact that has contributed to the more than 100% increase in cases of chronic low back pain (CLBP). This condition represents the leading cause of disability worldwide.

The diagnostic evaluation of patients with back pain can be very challenging and requires complex clinical decision-making. One of the main challenges faced by specialists is to discover which of the potentially involved musculoskeletal structures is or are the source of the pain. This is a key factor in managing these patients and avoiding therapeutic errors.

5

out of every six people experience back pain in adulthood

detailGrey

Only 15% of patients with back pain are diagnosed with a specific type of low back pain.

Magnetic resonance imaging (MRI) has become the imaging technique of choice for assessing spinal disorders, almost completely replacing CT in the routine study of degenerative disc disease, infections, trauma, neoplasms, and spinal cord disorders.

The aim of this project is to incorporate artificial intelligence tools through the design of a convolutional neural network for the processing of magnetic resonance images for the cataloguing and grading of low back pain.

To achieve the objectives

2019-2022

To carry out the project, Atrys will collaborate with the VICOMTECH technology centre, an applied research centre integrated in the CERVERA centre network, specialised in the development and innovation in information technologies. This is especially the case regarding the convergence of computer graphics and computer vision (visual computing), data analytics & intelligence, interactive digital media, and language technologies.

This project has the support of CDTI for its funding through the call for Cervera Grants for Technology Centres.

Financial backers

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