iTRIAL, driving the digital transformation of clinical trials with AI and medical imaging-based solutions

Vicomtech has actively participated in the development of an innovative solution for the management of clinical trials, conceived and validated within the framework of a Pre-Commercial Public Procurement (PCP) contract. The iTRIAL project, promoted in collaboration with the Galician Health Service (SERGAS) and research foundations, responds to a strategic initiative aimed at the digital transformation of the healthcare sector. It has also received the support and involvement of the Centre for the Development of Technology and Innovation (CDTI), which has played a key role in driving both the process and the procurement procedure. The project has been coordinated by NTT Data, with whom we have worked closely, highlighting the value of cooperation and synergy between public and private entities in addressing complex challenges in the healthcare field.

The initial results of the pilot trials carried out at SERGAS have been very positive, demonstrating the value of using precision medicine tools in clinical research. Although the solution has already been developed and tested, its operational deployment is planned for a later phase.

Thanks to the collaboration between Vicomtech’s medical imaging and predictive medicine areas, two key modules have been developed to make clinical trial management more efficient and accurate: a Medical Imaging Annotation Module and a Genomic Data Annotation and Prioritisation Module.

AI-based Medical Imaging Annotation Module

The Medical Imaging Annotation Module combines an advanced DICOM viewer (the standard format for medical images) with tools that allow medical images to be annotated manually or automatically using artificial intelligence. This makes it easier to analyse images in a structured way, store reusable annotations and select clinical trial candidates more quickly and reliably.

Genomic Data Annotation and Prioritisation Module

The Genomic Data Module automatically processes and classifies genetic variants from VCF files, using information from reference databases such as ClinVar and CPIC. It also allows rules to be defined based on patients’ genetic profiles, helping to select trial participants in a more accurate and efficient manner.

Overall, these advances demonstrate that integrating medical imaging, genomic data and artificial intelligence provide real value in the management of clinical trials. The solutions developed have great potential to improve clinical research and its application in hospitals, contributing to faster decision-making, more personalized care and greater efficiency in the healthcare system.