Respiratory diseases are among the leading causes of mortality and disability worldwide. Diffuse interstitial lung diseases (DILD), such as idiopathic pulmonary fibrosis (IPF) or sarcoidosis, which cause progressive scarring of the lung tissue, impairing respiratory capacity and the body's ability to supply adequate oxygen, are of particular concern.

Diagnosing these types of pathologies is not easy as medical imaging often shows characteristics that overlap between different diseases, making it difficult to interpret. Furthermore, once the diagnosis has been made, there are currently no factors capable of predicting the course of the disease or its response to treatment. This is important in this type of disease, which has a high morbidity and mortality rate, where correct treatment has been shown to slow the disease, but delayed diagnosis and suboptimal treatment are associated with a worse prognosis.

Detecting this type of disease is not easy, as medical images often show similar signs between different pathologies, which makes analysis challenging. Furthermore, once the diagnosis is confirmed, there are still no tools available to predict how the disease will progress or how it will respond to treatment. This is particularly important because these are serious diseases with a high likelihood of complications or even death. It has been shown that proper treatment can slow their progress, but if diagnosis is delayed or treatment is inadequate, the prognosis worsens.

With this in mind, the multinational tech company GMV, in collaboration with the Hospital Universitario La Paz and the Universidad Complutense de Madrid, has taken up the challenge proposed by the Center for Technological Development and Innovation (CDTI): developing a simulator based on artificial intelligence (AI) that makes it possible to understand and predict the course of interstitial lung diseases.

Disruptive advances in medical imaging

The GMV-developed simulator will use advanced analysis of computed tomography (CT) images, enabling the artificial intelligence to pinpoint all visible patterns in X-rays related to DILDs. It will also be able to determine the main pattern and indicate if it is a fibrotic disease.

Additionally, one of the project's most innovative features is the AI's ability to predict the course of the disease by integrating medical imaging with respiratory function tests. This predictive capacity is expected to help specialists predict how the disease will progress and adapt treatments to each patient's specific needs, significantly improving their quality of life.

Beyond lung diseases, this project lays the foundations for a broader integration of artificial intelligence in other medical areas, thanks to a multidisciplinary approach that brings together experts in biomedical engineering, radiology, pulmonology, and AI.

The company just presented its design proposal as part of the first phase of the pre-commercial public procurement launched by the CDTI. This project is financed with the center's own funds and through the Recovery and Resilience Mechanism (RRM), within the Recovery, Transformation and Resilience Plan (RTRP), financed by the European Union - NextGenerationEU.