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2020 ; 8
(7
): 450
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Deep learning for detecting corona virus disease 2019 (COVID-19) on
high-resolution computed tomography: a pilot study
#MMPMID32395494
Yang S
; Jiang L
; Cao Z
; Wang L
; Cao J
; Feng R
; Zhang Z
; Xue X
; Shi Y
; Shan F
Ann Transl Med
2020[Apr]; 8
(7
): 450
PMID32395494
show ga
BACKGROUND: To evaluate the diagnostic efficacy of Densely Connected
Convolutional Networks (DenseNet) for detection of COVID-19 features on high
resolution computed tomography (HRCT). METHODS: The Ethic Committee of our
institution approved the protocol of this study and waived the requirement for
patient informed consent. Two hundreds and ninety-five patients were enrolled in
this study (healthy person: 149; COVID-19 patients: 146), which were divided into
three separate non-overlapping cohorts (training set, n=135, healthy person,
n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10;
test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained
and tested to classify the images as having manifestation of COVID-19 or as
healthy. A radiologist also blindly evaluated all the test images and rechecked
the misdiagnosed cases by DenseNet. Receiver operating characteristic curves
(ROC) and areas under the curve (AUCs) were used to assess the model performance.
The sensitivity, specificity and accuracy of DenseNet model and radiologist were
also calculated. RESULTS: The DenseNet algorithm model yielded an AUC of 0.99
(95% CI: 0.958-1.0) in the validation set and 0.98 (95% CI: 0.972-0.995) in the
test set. The threshold value was selected as 0.8, while for validation and test
sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the
specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively.
The sensitivity of radiologist was 94%, the specificity was 96%, while the
accuracy was 95%. CONCLUSIONS: Deep learning (DL) with DenseNet can accurately
classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss
diagnosis rate (combined with radiologists' evaluation) and radiologists'
workload.
free
free
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