Respiratory diseases are among the leading causes of mortality and disability worldwide. Among them, diffuse interstitial lung diseases (DILD), such as idiopathic pulmonary fibrosis (IPF) and sarcoidosis, cause progressive scarring of lung tissue, impairing respiratory function and oxygen supply to the body. Diagnosing these conditions is challenging, as medical imaging often shows overlapping characteristics between different diseases, complicating interpretation. Furthermore, once diagnosed, there are no tools to predict disease progression or treatment response, which is critical in such high-morbidity diseases. Delayed diagnosis or suboptimal treatment often worsens the prognosis.
In response to this challenge, GMV, in collaboration with La Paz University Hospital and the Complutense University of Madrid, is developing an AI-based simulator to predict the progression of interstitial lung diseases. This project, part of a public procurement initiative funded by the European Union’s NextGenerationEU program, aims to improve diagnostic accuracy and treatment outcomes.
Artificial intelligence, particularly deep learning, is a key tool in the future of medical diagnostics. Medical imaging plays a vital role in 80% of medical decisions, but interpreting images for DILDs remains complex. GMV’s simulator uses advanced CT scan analysis to accurately identify patterns associated with DILDs, distinguishing between fibrotic and non-fibrotic diseases. By integrating imaging with respiratory function tests, the AI can predict disease evolution, enabling personalized treatment plans that improve patient outcomes and quality of life.
Article written by GMV team
02/04/2025
Source:
GMV