Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017. The dataset for this competition is consisted of 12.6k radiological images of left hand labeled by the bone age and sex of patients. Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and inference. This approach allows us to measure importance of specific hand bones for the automated bone age analysis. We further evaluate performance of the method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.Comment: 14 pages, 9 figure

Development of a 3D structural MRI preprocessing pipeline based on advanced normalization tools (ANTs)

Para distinguir el envejecimiento cerebral normal del envejecimiento patológico, se requiere un estudio de la estructura del cerebro a través de resonancias magnéticas. No obstante, dichas imágenes suelen presentar varios problemas que dificultan la extracción de conclusiones, por lo que se deben someter a una serie de técnicas de procesado de la imagen relacionadas con normalizaciones. Esta tesis estudia el uso de Advanced Normalization Tools (ANTs) como núcleo del procesado, complementado con un sistema de control de calidad para evaluar sus resultados y un método para monitorear el uso de recursos computacionales de todo el proceso, además de explorar cómo usar los datos extraídos para alimentar un sistema Machine Learning de predicción de la edad cerebral. Los resultados de esta tesis incluyen: la forma óptima de usar ANTs en este contexto, un sistema de correlación de volúmenes para evaluar sus resultados, un sistema simple de registro del uso de recursos basado en comandos de Linux y los resultados de alimentar diferentes los datos extraídos al sistema de predicción de la edad cerebral XGBoost.To tackle distinguishing normal brain aging from pathological aging, MRI scans are often used to study the brains' structure. Nevertheless, these scans frequently present several problems that harden extracting conclusions, so they have to undergo a series of image processing techniques to improve their usefulness. This thesis studies the use of Advanced Normalization Tools (ANTs) as the core of the processing pipeline, complemented by a quality check system to assess its outputs and a method to monitor the computational resource usage of the whole process, in addition to extracting structural data to feed a Machine Learning brain age prediction system. The outcomes of this thesis include the optimal way to use ANTs in this context, a volume correlation system to evaluate its results, a simple Linux command based resource usage summarisation program and the results from feeding the extracted data to a XGBoost brain age prediction system.Per distingir l'envelliment cerebral normal de l'envelliment patològic, cal un estudi de l'estructura del cervell a través de ressonàncies magnètiques. Tot i això, aquestes imatges solen presentar diversos problemes que dificulten l'extracció de conclusions, per la qual cosa s'han de sotmetre a una sèrie de tècniques de processament de la imatge relacionades amb normalitzacions. Aquesta tesi estudia l'ús d'Advanced Normalization Tools (ANTs) com a nucli del processat, complementat amb un sistema de control de qualitat per avaluar-ne els resultats i un mètode per monitoritzar l'ús de recursos computacionals de tot el procés, a més d'explorar com fer servir les dades extretes per alimentar un sistema Machine Learning de predicció de l'edat cerebral. Els resultats d'aquesta tesi inclouen: la forma òptima de fer servir ANTs en aquest context, un sistema de correlació de volums per avaluar els seus resultats, un sistema simple de registre de l'ús de recursos basat en comandes de Linux i els diversos resultats d'alimentar les dades extretes al sistema de predicció de l'edat cerebral XGBoost