Working on enhancing the services of the PARADIM platform, which focuses on medical image annotation, reuse, and analysis.

Developing and optimizing features for efficient management and analysis of medical images.

Integrating new tools and extensions to improve the platform's functionality and user experience.
 

I work on improving the services of the PARADIM platform, which focuses on the annotation, reuse and analysis of medical images. The objective is to develop and optimize existing functionalities to have effective management and analysis of medical images. This involves the integration of new tools and extensions, to improve the platform's functionality and the user experience.

Synthetic healthcare datasets are useful to support the development of data analysis and machine learning techniques in healthcare, by offering access to representative data to experiment and generate models from while mitigating the issues associated with dealing with highly sensitive data related to human subjects. However, the performance and usefulness of data analysis and machine learning methods applied depend on the quality of these synthetic datasets and their representativity of the phenomenon to model.

As tools derived from artificial intelligence are used more frequently in medicine and health-related domains, understanding their predictions becomes increasingly important when determining the trustworthiness of a prediction. 

One of the primary challenges of diagnosing Alzheimer’s Disease (AD) lies in its progression through two silent decades. The lack of symptoms in patients during this time evidently hinders their chance of suspecting the disease, or merely being granted a precautionary brain scan. Moreover, the initial endogenous signs and noticeable symptoms often coincide with aging individuals without any neurological disease diagnosis.

It is often difficult, even sometimes impossible, to share denominalized data between organisations and researchers due to ethical constraints regarding participant confidentiality. Synthetic datasets could facilitate data sharing. However, many current methods, which use multiple imputation (MI) techniques for missing data, lower the analysis potential and the quality of the results.

While some studies report the positive effects of continuing professional development (CPD) on clinical behaviour, few address the sustainability of these effects as well as the types of approaches that could improve this sustainability.