Rocket is a unified (cloud-based) platform aimed at assisting clinicians, allowing them to store, visualize and process different types of clinical data: measurements, images, and reports. Being web-based, it allows end-users to access patient information using their smart-phones, tablets or computers, with the only need of internet connection. The platform integrates a computation engine to analyze data in real time, through different methodologies such as decision trees from clinical guidelines, data mining or machine learning, geared toward the improvement of clinical decision making.

One of the key pillars of this platform is the viewer. The Rocket viewer allows visualizing different kinds of data such as medical and biological images, 3D surfaces, electric signals (ECGs) and documents, either from the web or loading information from the local file system. Its code is available open source at the GitHub repository of BCN MedTech where further description, tutorials, and demo videos can be found. The original developers of Rocket are Carlos Yagüe Méndez and  María del Pilar García Gracia, both from the Department of Information and Communication Technologies (DTIC), under the supervision of the research professor ICREA  Bart Bijnens, the professor Oscar Camara and the doctoral student Sergio Sánchez.

Rocket platform to explore population clinical data to identify high risk pregnancies

The Rocket platform is already being used in a project granted by the Bill & Melinda Gates Foundation, in collaboration with The Aga Khan University (Karachi, Pakistan).  Pakistan is one of the countries where stillbirth rate and early neonatal mortality rates are among the highest in the world. Routine screening of neonatal and placental blood flow through Doppler echocardiography is needed to recommend interventions during pregnancy that can drastically reduce perinatal deaths. However, in developing countries, and especially in rural or challenged regions, the lack of fetal medicine specialists hinders the implantation of these routine screening techniques.

The first objective of this project is to use the Rocket platform to allow fetal medicine specialists to store, share and visualize clinical data, at anytime, anywhere, so they can remotely take actions to reduce the burden of perinatal outcomes. Building on this, another objective is to implement data visualization tools within the Rocket platform to explore population clinical data to identify high risk pregnancies, and to explore machine learning algorithms trained with echocardiographic studies to predict those cases at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities. Olga Galí i Pérez, a student of the Bachelor’s degree in Biomedical Engineering of UPF, has recently travelled to Karachi to work towards the integration of the Rocket Platform with the data repositories there.

Developing a clinical interface for patients with inherited disorders causing sudden cardiac death

 “In the next months, we plan to use the Rocket platform in a collaboration with Dra Begoña Benito from Hospital del Mar and researcher at the Institut Hospital del Mar d’Investigacions Mediques (IMIM), which aims at developing a clinical interface for managing and processing familiar data from patients with inherited disorders causing sudden cardiac death. This interface may help clinicians to further understand and prevent such inherited disorders from a family point of view”, said developer’s team.  

This work has been funded during recent years by the European VP2HF, CardioFunXion, NEUBIAS projects and the Maria de Maetzu del DTIC strategic research program from the DTIC that promotes research on the extraction of data-driven knowledge.

The Rocket platform will soon be available at the GitHub repository of BCN MedTech.

 (https://github.com/bcnmedtech/rocket_viewer)