This project investigates the application of geometric morphometrics and machine learning to support the diagnosis and treatment planning of SCIII. By leveraging quantitative shape analysis of craniofacial structures, the study aims to identify clinically meaningful subphenotypes and develop predictive models to assist clinical decision-making.
Os sistemas de saúde estão cada vez mais sob pressão, desde a escassez de profissionais até aos tempos de espera mais longos e ao excesso de carga administrativa. Neste contexto, a Inteligência Artificial surge como uma ferramenta prática para apoiar os profissionais, melhorar os cuidados aos doentes e ajudar os sistemas a gerir a crescente procura. Em 2026, o foco já não é se a IA vai transformar a saúde, mas como poderá fazê-lo à escala.
Healthcare systems are under growing strain, from staff shortages to longer waiting times and administrative overload. In this context, Artificial Intelligence is emerging as a practical tool to support professionals, improve patient care, and help systems manage rising demand. In 2026, the focus is no longer if AI will shape healthcare, but how it can do so at scale.
The iSTARS project is pleased to support the launch of REDCap at the Lisbon School of Medicine, making a widely used research data management platform available to the FMUL research community.
Baber, R. W., Magalhães-Patrício, M. Â., Pires, M. V., Correia, V., Lozano-Gil, J. M., Guimarães, J. C., Brown, C. C., Dalod, M., & Minutti, C. M. [...]
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iSTARS Data Scientist
Tel. +351 217 999 422
Address. Avenida Professor Egas Moniz, 1649-028 Lisboa — Portugal
Email. istars@medicina.ulisboa.pt
The project iSTARS received funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 952377
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