New X-ray technology can improve COVID-19 diagnosis TU_Muenchen NaturePortfolio
Institutional Review Board and national radiation protection agency approval Z5-22462/2-2017-021 and Z5–22464/2020-047-G) was obtained prior to this study. Patients gave their written informed consent prior to study participation.illustrates COVID-19 patient selection. Between May 2020 and December 2020, patients of legal age that underwent chest CT at our institution as part of their diagnostic workup and with a clinically suspected COVID-19 infection were screened for study participation.
Exclusion criteria were a negative RT-PCR test within 2 days before the CT scan, pregnancy, lung cancer, and pneumothorax. Sixty patients with suspected COVID-19 infection were included in this study.Between October 2018 and January 2020, patients of legal age that underwent chest CT at our institution as part of their diagnostic workup were screened for study participation. All CT images of potential study participants were analysed for pathologic lung changes by three radiologists .
Attenuation-based images were additionally evaluated by using the winning neural network of the SIIM-FISABIO-RSNA COVID-19 Detection ChallengeThe quantitative dark-field coefficient was calculated according to Gassert et al.The area under the receiver operating characteristic curve was calculated for all three reading modalities, and AUC values were tested for differences with Obuchowski’s method for correlated and clustered ROC data.
As images were acquired in a clinical setting and COVID-19-associated lung changes change over time, reproduction of image acquisition is not feasible. Statistical analysis of acquired images, however, is reproducible.
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