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End of Master Project (TFM)  

 

Design and implementation of a diagnosis platform to assist Kawasaki disease identification

The Kawasaki disease is the most common heart condition affecting young children, usually under five years old, in developed countries. The disease is responsible for the damages of blood vessels all over the body and results in vasculitis, myocarditis and coronary dilation causing long term heart complications. Therefore, it is essential to be able to detect the disease at an early state.

One of the methods used to detect Kawasaki disease is by the analysis of the echocardiograms of the heart. In the Grupo de Tratamiento de Imágenes we have already developed classification and segmentation algorithms based on Deep Learning techniques for the echocardiograms. This Master Thesis will aim the integration and improvement of both algorithms within a common platform. Besides, this platform will also address the final diagnosis of the Kawasaki disease from echocardiograms and clinical data based also on Deep Learning techniques.

This Master Thesis will be done in collaboration with Hospital 12 de Octubre.

We are looking for students with experience with Python and Deep Learning Tools ( Keras, Tensorflow, PyTorch, …)

Contact person: Julián Cabrera, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Design and implementation of a breath-rate measurement solution based on computer vision and deep learning techniques

According to the last global estimate of pneumonia mortality, nearly one million children die from pneumonia worldwide every year, accounting for 16% of child deaths globally. Despite its prevalence, childhood pneumonia remains difficult to diagnose, particularly in those contexts where diagnostic imaging is unavailable and is based on subjective clinical signs and symptoms such as fast breathing and chest indrawing.

Accurate assessment of the respiratory rate is critical in Low Income Countries where other diagnostic tools, such as pulse oximetry or chest radiography, are not available. In this sense, this Master Thesis addresses the design and development of a solution for measuring the respiratory rate from a controlled video capture of the child using a smartphone. The approach will be based on computer vision and deep learning techniques.

This Master Thesis will be done in collaboration with Hospital 12 de Octubre.

We are looking for students with knowledge and experience with Python, Computer Vision techniques and SW tools, and Deep Learning Tools ( Keras, Tensorflow, PyTorch, …)

Contact person: Julián Cabrera, This email address is being protected from spambots. You need JavaScript enabled to view it.