ProgNet: COVID-19 Prognosis Using Recurrent and Convolutional Neural Networks

Abstract

Aims: Prognosis of lung mathology severity after Covid-19 infection using chest X-ray time series Background: We have been inspired by methods analysing time series of images in remote sensing for change detection. During the current Covid-19 pandemic, our motivation is to provide an automatic tool to predict severity of lung pathologies due to Covid-19. This can be done by analysing images of the same patient acquired at different dates. Since no analytical model is available, and also no accurate quantification tools can be used due to many unknowns about the pathology, feature-free methods are good candidates to analyse such temporal images. Objective: This contribution helps improving performances of medical structures facing the Covid-19 pandemic. The first impact is medical and social since more lives could be saved with a 92% rate of good prognosis. In addition to that, patients in intensive care units (up to 15%) could a posteriori suffer from less sequels due to an early and accurate prognosis of their PP. Moreover, accurate prognosis can lead to a better planning of patient’s transfer between units and hospitals, which is linked to the second claimed economical impact. Indeed, prognosis is linked to lower treatment costs due to an optimized predictive protocol using ragiological prognosis. Methods: Using Convolutional Neural Networks (CNN) in combination with Recurrent Neural Networks (RNN). Spatial and temporal features are combines to analyse image time series. A prognosis score is delivered indicating the severity of the pathology. Learning is made on a publicly available database. Results: When applied to radiological time-series, promising results are obtained with an accuracy rates higher than 92%. Sensitivity and specificity rates are also very interesting. Conclusion: Our method is segmentation-free, which makes it competitive with respect to other assessment methods relying on time-consuming lung segmentation algorithms. When applied on radiographic data, the proposed ProgNet architecture showed promising results with good classification performances, especially for ambiguous cases. Specifically, the reported low false positive rates are interesting for an accurate and personalised care workflow.

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

“…To evaluate the COVID-19 course in patients for prognosis analysis, a deep model that leverages RNN and CNN architectures to assess the temporal evolution of images was proposed by Fakhfakh et al (2020). The multi-temporal classification of X-ray images, together with clinical and radiological features, is considered as the foundation of prognosis and assesses COVID-19 infection evolution in terms of positive/negative evolution.…”

COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

“…Other researchers have also tried to tackle the same problem using CT images, reaching high scoring metrics and precise abnormality localization [24,25]. Contrarily, even though many studies have claimed to reach excellent classification accuracy scores using CXRs [26,27,28,29,30,31,32], none of them have reported visualization results for their model decisions. Considering the fact that pneumonia diagnosis is more challenging in CXRs in comparison with CT scans and the available COVID-19 pneumonia CXR datasets are small, we investigate those studies with visual interpretability as it could be considered as a stronger performance metric.…”

Non-smooth Bayesian learning for artificial neural networks

“… 2021 ; Sajja and Kalluri 2021 ; Fakhfakh et al. 2020a ; Ostad-Ali-Askari et al. 2017 ; Ostad-Ali-Askari and Shayan 2021 ) are one of the state-of-art deep learning techniques.…”

“…One of the benefits and difficulties at the same time of DL is the ability to learn from massive amounts of data. In the same sense, Convolutional neural networks (CNN) Drewek-Ossowicka et al (2021); Sajja and Kalluri (2021); Fakhfakh et al (2020a); Ostad-Ali-Askari et al ( 2017); Ostad-Ali-Askari and Shayan (2021) are one of the state-of-art deep learning techniques. CNNs are designed to automatically and adaptively learn spatial hierarchies of features through backpropagation Rumelhart et al (1986) by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers.…”