Jean-Gabriel Minonzio | Universidad de Valparaíso, Escuela de Ingeniería en Informática;
Ana Aguilera | Escuela de Ingeniería en Informática; José Luis Dinamarca | Hospital Dr Gustavo Fricke; Viviana Garcia | Geropolis
Competition Sponsor: Chile Agencia Nacional de Investigacion y Desarrollo
Awardee Year: 2023
Osteoporosis and associated fragility fractures remain an increasing worldwide burden for both health systems and families, in the context of ageing populations. Hip fractures are particularly severe due to the hospital stay, operations, arduous recovery and risk of subsequent fractures. Thus, it is of significant importance to detect patients at high risk of femoral fragility fractures and to anticipate their recovery capacities in order to take appropriate medical decisions.
The current gold standard for osteoporosis remains the Dual-energy X-ray absorptiometry (DXA), however one the one hand, a majority of fractured patients are not classified as osteoporotic using the WMO definition and on the other hand, DXA is not widely available in numerous places. Nowadays, growing accessibility to clinical data, processing methods and computing power, opened the way to novel data driven prediction models using a large number of biomarkers or parameters, opening perspective towards personalized precision medicine.
The aim of the project is to build a prediction model of hip fragility fracture using hospital data routinely collected in the traumatology department from the last years and up to date Explainable Artificial Intelligence (XAI) tools. This model should be adapted to the “real world” conditions of the region and predict clinical data such as risk of fracture and refracture, mortality risk, fracture type classification and the generation of a specific comorbidity index.