3 research outputs found
FEATURES OF ICU ADMISSION IN X-RAY IMAGES OF COVID-19 PATIENTS
X-ray images may present non-trivial features with predictive information of
patients that develop severe symptoms of COVID-19. If true, this hypothesis may
have practical value in allocating resources to particular patients while using
a relatively inexpensive imaging technique. The difficulty of testing such a
hypothesis comes from the need for large sets of labelled data, which need to
be well-annotated and should contemplate the post-imaging severity outcome.
This paper presents an original methodology for extracting semantic features
that correlate to severity from a data set with patient ICU admission labels
through interpretable models. The methodology employs a neural network trained
to recognise lung pathologies to extract the semantic features, which are then
analysed with low-complexity models to limit overfitting while increasing
interpretability. This analysis points out that only a few features explain
most of the variance between patients that developed severe symptoms. When
applied to an unrelated larger data set with pathology-related clinical notes,
the method has shown to be capable of selecting images for the learned
features, which could translate some information about their common locations
in the lung. Besides attesting separability on patients that eventually develop
severe symptoms, the proposed methods represent a statistical approach
highlighting the importance of features related to ICU admission that may have
been only qualitatively reported. While handling limited data sets, notable
methodological aspects are adopted, such as presenting a state-of-the-art lung
segmentation network and the use of low-complexity models to avoid overfitting.
The code for methodology and experiments is also available
MAVIDH Score: A COVID-19 Severity Scoring using Chest X-Ray Pathology Features
The application of computer vision for COVID-19 diagnosis is complex and
challenging, given the risks associated with patient misclassifications.
Arguably, the primary value of medical imaging for COVID-19 lies rather on
patient prognosis. Radiological images can guide physicians assessing the
severity of the disease, and a series of images from the same patient at
different stages can help to gauge disease progression. Based on these
premises, a simple method based on lung-pathology features for scoring disease
severity from Chest X-rays is proposed here. As the primary contribution, this
method shows to be correlated to patient severity in different stages of
disease progression comparatively well when contrasted with other existing
methods. An original approach for data selection is also proposed, allowing the
simple model to learn the severity-related features. It is hypothesized that
the resulting competitive performance presented here is related to the method
being feature-based rather than reliant on lung involvement or compromise as
others in the literature. The fact that it is simpler and interpretable than
other end-to-end, more complex models, also sets aside this work. As the data
set is small, bias-inducing artifacts that could lead to overfitting are
minimized through an image normalization and lung segmentation step at the
learning phase. A second contribution comes from the validation of the results,
conceptualized as the scoring of patients groups from different stages of the
disease. Besides performing such validation on an independent data set, the
results were also compared with other proposed scoring methods in the
literature. The expressive results show that although imaging alone is not
sufficient for assessing severity as a whole, there is a strong correlation
with the scoring system, termed as MAVIDH score, with patient outcome
Features of ICU admission in x-ray images of Covid-19 patients
This paper presents an original methodology for extracting semantic features from X-rays images that correlate to severity from a data set with patient ICU admission labels through interpretable models. The validation is partially performed by a proposed method that correlates the extracted features with a separate larger data set that does not contain the ICU-outcome labels. The analysis points out that a few features explain most of the variance between patients admitted in ICUs or not. The methods herein can be viewed as a statistical approach highlighting the importance of features related to ICU admission that may have been only qualitatively reported. In between features shown to be over-represented in the external data set were ones like ‘Consolidation’ (1.67), ‘Alveolar’ (1.33), and ‘Effusion’ (1.3). A brief analysis on the locations also showed higher frequency in labels like ‘Bilateral’ (1.58) and Peripheral (1.28) in patients labelled with higher chances to be admitted in ICU. To properly handle the limited data sets, a state-of-the-art lung segmentation network was also trained and presented, together with the use of low-complexity and interpretable models to avoid overfitting