Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes.
Radiol Artif Intell
; 4(1): e210105, 2022 Jan.
Article
em En
| MEDLINE
| ID: mdl-35146436
ABSTRACT
PURPOSE:
To determine if the mean curvature of isophotes (MCI), a standard computer vision technique, can be used to improve detection of chronic obstructive pulmonary disease (COPD) at chest CT. MATERIALS ANDMETHODS:
In this retrospective study, chest CT scans were obtained in 243 patients with COPD and 31 controls (among all 274 151 women [mean age, 70 years; range, 44-90 years] and 123 men [mean age, 71 years; range, 29-90 years]) from two community practices between 2006 and 2019. A convolutional neural network (CNN) architecture was trained on either CT images or CT images transformed through the MCI algorithm. Separately, a linear classification based on a single feature derived from the MCI computation (called hMCI1) was also evaluated. All three models were evaluated with cross-validation, using precision-macro and recall-macro metrics, that is, the mean of per-class precision and recall values, respectively (the latter being equivalent to balanced accuracy).RESULTS:
Linear classification based on hMCI1 resulted in a higher recall-macro relative to the CNN trained and applied on CT images (0.85 [95% CI 0.84, 0.86] vs 0.77 [95% CI 0.75, 0.79]) but with a similar reduction in precision-macro (0.66 [95% CI 0.65, 0.67] vs 0.77 [95% CI 0.75, 0.79]). The CNN model trained and applied on MCI-transformed images had a higher recall-macro (0.85 [95% CI 0.83, 0.87] vs 0.77 [95% CI 0.75, 0.79]) and precision-macro (0.85 [95% CI 0.83, 0.87] vs 0.77 [95% CI 0.75, 0.79]) relative to the CNN trained and applied on CT images.CONCLUSION:
The MCI algorithm may be valuable toward the automated detection and diagnosis of COPD on chest CT scans as part of a CNN-based pipeline or with stand-alone features.Keywords Chronic Obstructive Pulmonary Disease, Quantification, Lung, CT Supplemental material is available for this article. See also the invited commentary by Vannier in this issue.© RSNA, 2021.
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Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article