Combining CT scans and clinical features for improved automated COVID-19 detection
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2021-09-01
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en
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Abstract
During the first peak of the COVID-19 pandemic, hospitals in hard-hit regions were
overflowing with patients at the emergency unit with respiratory complaints. Since
the RT-PCR test was in limited supply at the time and test results took a long time
to obtain, many hospitals opted to use chest CT scans of COVID-19 suspects. As a
result of this, several studies examined the possibility of automating the detection of
COVID-19 in CT scans. One such study, by Lessmann et al., 2020, developed a model
to predict COVID-19 severity scores based on these chest CT scans. In this thesis, we
extended their model in several ways to take into account additional clinical values
(such as blood values, sex, and age) to predict either PCR outcomes or clinical diagnoses.
Based on data from the Canisius-Wilhelmina Ziekenhuis (CWZ) hospital
and Radboudumc hospitals, as well as the COVID-19 dataset by Ning et al., 2020,
we found that integrating these two modalities can indeed lead to improved performance
when both clinical and visual features are of sufficient quality. When training
on data from the CWZ hospital and evaluating on data from the Radboudumc hospital,
models using only clinical features or visual features achieved Area Under
the ROC Curve (AUC) values of 0.773 and 0.826, respectively; their combination
resulted in an AUC of 0.851. Similarly, when training on data from the Union hospital
in the iCTCF dataset and predicting on data from the Union hospital in that
same dataset, we obtained AUCs of 0.687 and 0.812 for clinical and visual features,
respectively; their combination resulted in an AUC of 0.862.
However, we also discovered that the patterns of missing data present in these
clinical feature datasets can play an essential role in the performance of the models
fitted on them. We thus developed additional methods to analyze and mitigate this
effect to obtain fairer evaluations and increase model generalizability. Still, the high
diagnostic performance of some of our models suggests that they could be adapted
into clinical practice, and our methods pertaining to missing data could be used to
aid further research using clinical feature datasets.
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Faculteit der Sociale Wetenschappen