Dataset Information

Dark-field chest X-ray imaging for the assessment of COVID-19-pneumonia.

ABSTRACT:

Background

Currently, alternative medical imaging methods for the assessment of pulmonary involvement in patients infected with COVID-19 are sought that combine a higher sensitivity than conventional (attenuation-based) chest radiography with a lower radiation dose than CT imaging.

Methods

Sixty patients with COVID-19-associated lung changes in a CT scan and 40 subjects without pathologic lung changes visible in the CT scan were included (in total, 100, 59 male, mean age 58 ± 14 years). All patients gave written informed consent. We employed a clinical setup for grating-based dark-field chest radiography, obtaining both a dark-field and a conventional attenuation image in one image acquisition. Attenuation images alone, dark-field images alone, and both displayed simultaneously were assessed for the presence of COVID-19-associated lung changes on a scale from 1 to 6 (1 = surely not, 6 = surely) by four blinded radiologists. Statistical analysis was performed by evaluation of the area under the receiver-operator-characteristics curves (AUC) using Obuchowski's method with a 0.05 level of significance.

Results

We show that dark-field imaging has a higher sensitivity for COVID-19-pneumonia than attenuation-based imaging and that the combination of both is superior to one imaging modality alone. Furthermore, a quantitative image analysis shows a significant reduction of dark-field signals for COVID-19-patients.

Conclusions

Dark-field imaging complements and improves conventional radiography for the visualisation and detection of COVID-19-pneumonia.

SUBMITTER: Frank M

PROVIDER: S-EPMC9678896 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Dark-field chest X-ray imaging for the assessment of COVID-19-pneumonia.

Frank Manuela M Gassert Florian T FT Urban Theresa T Willer Konstantin K Noichl Wolfgang W Schick Rafael R Schultheiss Manuel M Viermetz Manuel M Gleich Bernhard B De Marco Fabio F Herzen Julia J Koehler Thomas T Engel Klaus Jürgen KJ Renger Bernhard B Gassert Felix G FG Sauter Andreas A Fingerle Alexander A AA Haller Bernhard B Makowski Marcus R MR Pfeiffer Daniela D Pfeiffer Franz F

Communications medicine 20221121 1

<h4>Background</h4>Currently, alternative medical imaging methods for the assessment of pulmonary involvement in patients infected with COVID-19 are sought that combine a higher sensitivity than conventional (attenuation-based) chest radiography with a lower radiation dose than CT imaging.<h4>Methods</h4>Sixty patients with COVID-19-associated lung changes in a CT scan and 40 subjects without pathologic lung changes visible in the CT scan were included (in total, 100, 59 male, mean age 58 ± 14 y ...[more]

PMID: 36411311

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Project description:ObjectivesTo study whether a trained convolutional neural network (CNN) can be of assistance to radiologists in differentiating Coronavirus disease (COVID)-positive from COVID-negative patients using chest X-ray (CXR) through an ambispective clinical study. To identify subgroups of patients where artificial intelligence (AI) can be of particular value and analyse what imaging features may have contributed to the performance of AI by means of visualisation techniques.MethodsCXR of 487 patients were classified into [4] categories-normal, classical COVID, indeterminate, and non-COVID by consensus opinion of 2 radiologists. CXR which were classified as "normal" and "indeterminate" were then subjected to analysis by AI, and final categorisation provided as guided by prediction of the network. Precision and recall of the radiologist alone and radiologist assisted by AI were calculated in comparison to reverse transcriptase-polymerase chain reaction (RT-PCR) as the gold standard. Attention maps of the CNN were analysed to understand regions in the CXR important to the AI algorithm in making a prediction.ResultsThe precision of radiologists improved from 65.9 to 81.9% and recall improved from 17.5 to 71.75 when assistance with AI was provided. AI showed 92% accuracy in classifying "normal" CXR into COVID or non-COVID. Analysis of attention maps revealed attention on the cardiac shadow in these "normal" radiographs.ConclusionThis study shows how deployment of an AI algorithm can complement a human expert in the determination of COVID status. Analysis of the detected features suggests possible subtle cardiac changes, laying ground for further investigative studies into possible cardiac changes.Key points• Through an ambispective clinical study, we show how assistance with an AI algorithm can improve recall (sensitivity) and precision (positive predictive value) of radiologists in assessing CXR for possible COVID in comparison to RT-PCR. • We show that AI achieves the best results in images classified as "normal" by radiologists. We conjecture that possible subtle cardiac in the CXR, imperceptible to the human eye, may have contributed to this prediction. • The reported results may pave the way for a human computer collaboration whereby the expert with some help from the AI algorithm achieves higher accuracy in predicting COVID status on CXR than previously thought possible when considering either alone.

Dataset Information

Dark-field chest X-ray imaging for the assessment of COVID-19-pneumonia.

Background

Methods

Results

Conclusions

Publications

Dark-field chest X-ray imaging for the assessment of COVID-19-pneumonia.

Similar Datasets

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