At this year’s Mobile World Congress, the Eurecat technology centre is showcasing a new system for identifying nodules which are indicators of potential lung cancer using a tool powered by artificial intelligence techniques and deep learning in particular. The project has been conducted with the Vall d’Hebron Barcelona Hospital Campus and has been supported by CIDAI (Centre of Innovation for Data tech and Artificial Intelligence).
The technology makes it possible to train predictive models using 3D medical imaging embedded in the radiology workflow to “enable early detection of disease and provide a tool to support prognosis and monitoring by expert medical professionals,” says Felip Miralles, the director of Eurecat’s Digital Health Unit. “It’s a precision medicine breakthrough which is reshaping clinical practice and the healthcare industry.”
The innovation supports radiologists in monitoring lung nodules by means of a system running tests drawing on deep learning which can spot nodules and re-identify them to provide a forecast of growth and the likelihood of them being cancerous.
Key to this innovation has been building an interface to conduct time-based analysis of lung nodules and deliver intuitive and informative visual review of the results to help clinicians make more accurate diagnoses.
The tool has been developed by Eurecat in partnership with the Vall d’Hebron Barcelona Hospital Campus and coordinated by the Hospital’s Diagnostic Imaging Service as part of its Deep Lung project. “The goal is to develop an AI tool applied to CT images for early detection of lung cancer,” says Dr Manel Escobar, Clinical Director of the Diagnostic Imaging Department at Vall d’Hebron University Hospital and a researcher at the Vall d’Hebron Research Institute (VHIR). “This tool is based on monitoring suspicious lesions as evaluated by Vall d’Hebron’s radiology specialists. The application of AI in these cases will enhance diagnostic and predictive capacity in patients affected by the disease and in future population screening programmes for lung cancer.”
