Team "Red Flags"

The project offers a compassionate online platform for parents of children who are at risk of cancer by providing vital information and building a supportive community.

  • Early Detection, Proactive Care: The introduces a cancer risk assessment tool. Users fill surveys, enabling the system to guage their cancer risk, and promote proactive health management
  • Nurturing Support: The platform becomes a safe haven for parents, offering guidance, resources, and emotional support throughout their cancer journey
  • Empowering Information: Parents access curated medical insights, treatment options, coping mechanisms, and success stories, enabling informed decisions
  • Revolutionizing Healthcare: Their ambitions expand to partnering with hospitals. By integrating the tool into patient data analysis, they enhance cancer detection, making early intervention standard.

Team "Alternative"

The team us currently working on a project that aims to develop an computer vision powered tool to improve diagnostics in the field of oncology. Currently, the diagnostic process is carried out manually by radiologists and oncologists, which heavily relies on human expertise. Alternative utilizes AI technology to quickly analyze radiograms, providing real-time support and guidance to healthcare specialists.

Team "PrevenGS"

Transcriptome data is a valuable resource to study cancer molecular mechanisms. Based on the current NGS data analysis pipeline and the transcriptome data visualization prototype available at the children's hospital, the team is building an NGS transcriptome analysis tool to determine and visualize gene expression for a cancer patient. The software module will be validated using clinically relevant data, so it can be used in a current bioinformatics data-analysis pipeline.

Team "19%"

Support tool for general practitioners (GPs) focused on automating screening engagement process and early diagnostics of colorectal cancer through structuring and analyzing histology reports using LLM

The primary objective is to construct a model that relies on well-structured histological description data. This model will empower the team to conduct retrospective data analysis, allowing to uncover trends, correlations, and other relevant insights. Furthermore, leveraging this model, they will be able to provide valuable recommendations to specialists.