SNeuronEye (SNI)
SNeuroEye is an AI-driven solution designed to generate automatic neuroradiology reports for diagnosing brain lesions in MRI scans. It combines computer vision for lesion detection, classification, and segmentation with natural language processing (NLP) to create structured, high-quality diagnostic reports. Radiologists can validate or refine the AI-generated reports, enhancing accuracy and efficiency. The platform also specializes in glioma subtyping. Initially developed as an educational tool for radiologists in training, SNeuroEye aims to reduce workload, streamline reporting, and improve patient care while addressing global radiologist shortages. The project includes potential partnerships with MRI vendors.
SNeuronEye (SNI)
Monika Pytlarz
Monika is a PhD student working on AI analysis of tumors, with a focus on gliomas. Her research combines radiology, histopathology, and genomics to improve tumor classification, and patient risk stratification, despite the biological heterogeneity of gliomas. The project’s computational image analysis of immunostaining provides insights into the tumor immune microenvironment, supporting development of more precise therapeutic strategies. She also investigates cross-modal image translation, including the synthesis of histology from MRI scans.
Monika obtained a BSc in Electroradiology from Collegium Medicum of the Jagiellonian University and an MSc in Bioinformatics from JU, and brings clinical experience from diagnostic imaging departments. She explores translating AI models into clinical practice via computer-aided diagnosis tools and PACS integration.
