The goal of the project is to develop an artificial intelligence-based IT decision support system for the field of oncology, built on a bioinformatics and imaging diagnostics foundation. This system aims to assist in early diagnostics and the development of personalized therapies.

The development has significant innovative value, as it includes the analysis of functional genomic correlations using machine learning methods, enabling the detection of previously unknown genomic markers critical for early diagnostics and therapeutic efficacy. Beyond examining the coding regions of the genome, additional genomic pattern details will also be analyzed, addressing a critical gap in current diagnostic and therapeutic services.

Another key innovation target of the project is the automation of tumor segmentation in PET/CT radiological image processing using deep learning methods, which is expected to enhance the early detection of tumor tissues.

The project's innovation spectrum is further broadened by objectives related to spatial simulation of genomes, enabling the comparison of spatial structural profiles of genomes derived from different cellular states. The resulting technology will facilitate the analysis of spatial genomic structural distortions in relation to functional genomics, mutations, and clinical data.

The disease type studied during the project is diffuse large B-cell lymphoma. A highlighted goal is to ensure flexible biomarker discovery for diagnostic and therapeutic decision support for other disease types as well.

The InnovITech is participating in the development as a leader, other consortium members: GE HealthCare, Széchenyi István University, clinical partners: University of Debrecen, Petz Aladár University Teaching Hospital.

Project ID: 2023-1.1.1-PIACI_FÓKUSZ-2024-00027

Contracted amount of subsidy: HUF 791 377 269