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RETRACTED: Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI
This article has been retracted: please see Elsevier policy on Article Correction, Retraction and Removal (https://www.elsevier.com/about/policies-and-standards/article-withdrawal).
This article has been retracted at the request of the authors. Following publication, the authors identified an error in the pre-processing of the data that brings the results of the article into question. Specifically, the collected datasets had been incorrectly concatenated and, as a result, datapoints across different measurement levels did not align properly. The authors rectified the error but found that their results are no longer valid.
Journal Article
X-ray dark-field chest radiography: a reader study to evaluate the diagnostic quality of attenuation chest X-rays from a dual-contrast scanning prototype
Objectives
To compare the visibility of anatomical structures and overall quality of the attenuation images obtained with a dark-field X-ray radiography prototype with those from a commercial radiography system.
Methods
Each of the 65 patients recruited for this study obtained a thorax radiograph at the prototype and a reference radiograph at the commercial system. Five radiologists independently assessed the visibility of anatomical structures, the level of motion artifacts, and the overall image quality of all attenuation images on a five-point scale, with 5 points being the highest rating. The average scores were compared between the two image types. The differences were evaluated using an area under the curve (AUC) based z-test with a significance level of
p
≤ 0.05. To assess the variability among the images, the distributions of the average scores per image were compared between the systems.
Results
The overall image quality was rated high for both devices, 4.2 for the prototype and 4.6 for the commercial system. The rating scores varied only slightly between both image types, especially for structures relevant to lung assessment, where the images from the commercial system were graded slightly higher. The differences were statistically significant for all criteria except for the bronchial structures, the cardiophrenic recess, and the carina.
Conclusions
The attenuation images acquired with the prototype were assigned a high diagnostic quality despite a lower resolution and the presence of motion artifacts. Thus, the attenuation-based radiographs from the prototype can be used for diagnosis, eliminating the need for an additional conventional radiograph.
Key Points
•
Despite a low tube voltage (70 kVp) and comparably long acquisition time, the attenuation images from the dark-field chest radiography system achieved diagnostic quality for lung assessment.
•
Commercial chest radiographs obtained a mean rating score regarding their diagnostic quality of 4.6 out of 5, and the grating-based images had a slightly lower mean rating score of 4.2 out of 5.
•
The difference in rating scores for anatomical structures relevant to lung assessment is below 5%.
Journal Article