| Project Detail |
AI-based framework to classify paediatric brain tumours
Paediatric brain tumours (PBTs) are the leading cause of cancer-related deaths in children and adolescents. AI technologies aid doctors in the detection and diagnosis of PBTs through clinical decision support systems (CDSS). However, due to limited medical image datasets, doctors require assistance in segmenting PBTs. Moreover, concerns about the lack of transparency in black-box AI models hinder the adoption of AI in CDSS among doctors. With the support of the Marie Sklodowska-Curie Actions programme, the DL-I-PBraTSC project aims to develop an advanced AI-based framework for classifying primary brain tumours (PBTs) in children and adolescents. This helps in diagnosing, planning treatment, and predicting patient outcomes. The project will gather large, balanced PBT medical images from a secondary hospital and use an online test platform to collect feedback.
The DL-I-PBraTSC project aims to address the significant impact of pediatric brain tumors (PBTs) as the leading cause of cancer death in children and adolescents. Artificial Intelligence (AI) technologies are increasingly being explored to assist doctors in detecting and diagnosing through clinical decision support systems (CDSS). However, They face the challenges in successfully segmenting PBTs due to the scarcity of available medical image datasets. Additionally, the lack of transparency in black-box AI models has raised concerns among doctors, hindering the adoption of AI in CDSS. To tackle these challenges, the project will develop a state-of-the-art interpretable AI-based framework to classify PBTs including tumor segmentation. DL-I-PBraTSC will identify the location of PBTs, classify of PBT types, and enable quantitative analysis of sub-region of PBT parameters helping clinicians in diagnosis, treatment planning, monitoring disease progression, and predicting patient outcomes. The project will start with collecting and preparing sufficiently large, balanced PBT medical images from secondment hospital with the assistance of medical experts. An online test platform will be implemented for clinicians to use the model, gathering feedback for further validation and improvements. The non-academic placement will provide real-world clinical validation of the models efficacy. The project findings will be shared in conferences or journals targeting both neuroscience and informatics. DL-I-PBraTSC can help healthcare providers make more informed decisions about diagnosis and treatment planning of PBTs and contribute to early detection and intervention. These can lead to better patient outcomes, improved overall healthcare delivery and public health outcomes, and reduced healthcare costs, aligning with the EUs objectives of providing ensuring the safety and well-being of its citizens and one of the Irish national research priorities areas, Health and Wellbeing. |
