DAPLIA, a project based on artificial neural networks for a better diagnosis of lumbar pathologies, comes to an end

Advances in Artificial Intelligence and the pathologies detection through images is a very present reality in current medical diagnoses. Thus, new technologies can reduce, to some extent, the workload of health professionals and thus, improve their reliability.

In this context, Vicomtech has worked hand in hand with Atrys, a healthcare services company, to develop an automatic diagnostic tool to detect lumbar pathologies in magnetic resonance images.

Specifically, the main challenges consisted of classifying pathologies at the level of the complete image, on the one hand, and locating regions of interest with public databases, on the other. Finally, and with the use of image processing technologies and Deep Learning models, the final challenge was to be able to combine both and be able to both locate the regions of interest and classify the pathologies detected.

As one of the Vicomtech researchers participating in DAPLIA, Javier Bóbeda, comments, "the main technical challenge in DAPLIA was to manage the variability in the magnetic resonance images, due to the fact that they were collected in several hospitals, with different types of acquisitions and the presence of multiple pathologies".

Vicomtech contributes to the development of medical devices that require advanced information processing, with extensive experience in specific medical image analysis and visualisation technologies. Thus, in DAPLIA, classical image processing has been used for the segmentation of the vertebrae and Deep Learning models for the combined classification of the regions of interest of the volumes for each pair of contiguous vertebrae.

In this way, Vicomtech highlights its commitment to contribute to the process of digital transformation of the health sector, applying technology and knowledge by collaborating with institutions and technology companies in the sector and the biomedical and research sector, with the aim of improving the patients’ quality of life.