DeepAI AI Chat
Log In Sign Up

Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography

by   Gustavo Carneiro, et al.

We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on deep learning, where features and classifier are automatically learned in a single optimisation process; and 2) a multi-stage framework based on the design and selection/extraction of hand-crafted radiomics features, followed by the classifier learning process. Experimental results, based on a dataset of 48 annotated chest CTs, show that the deep learning model produces a mean 5-year mortality prediction accuracy of 68.5 (depending on the feature selection/extraction method and classifier). The successful development of the proposed models has the potential to make a profound impact in preventive and personalised healthcare.


Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning

Cardiovascular disease (CVD) is a leading cause of death in the lung can...

Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images

Known for its high morbidity and mortality rates, lung cancer poses a si...

Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coro...