Objective: The American College of Radiology recommends that mammogram images be viewed at 100% resolution (also called one-to-one or full resolution). We tested the effect of this and three other levels of zooming on the ability of radiologists to identify malignant calcifications on screening mammographic views.
Materials And Methods: Seven breast imagers viewed 77 mammographic images, 32 with and 45 without malignant microcalcifications, using four different degrees of monitor zooming. The readers indicated whether they thought a cluster of potentially malignant calcifications was present and where the cluster was located. Tested degrees of zooming included fit screen, a size midway between fit screen and 100%, 100%, and a size slightly larger than 100%.
Results: Readers failed to detect 17 clusters of malignant calcifications with fit-screen images, 12 clusters with midway images, 13 clusters with 100% images, and 11 clusters with slightly larger images. When viewing images without malignant microcalcifications, the readers marked false-positive areas on 25 images using fit-screen images, 43 of the midway images, 40 of the 100% images, and 29 of the slightly larger images.
Conclusion: All four tested levels of zooming functioned well. There was a trend for the fit-screen images to function slightly less well than the others with regard to sensitivity, so it may not be prudent to rely on those images without other levels of zooming. The 100% resolution images did not function noticeably better than the others.
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http://dx.doi.org/10.2214/AJR.10.5238 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
Biomed Phys Eng Express
January 2025
University of Gothenburg, Bruna stråket 13, Goteborg, 405 30, SWEDEN.
Dual-polarity readout is a simple and robust way to mitigate Nyquist ghosting in diffusion-weighted echo-planar imaging but imposes doubled scan time. We here propose how dual-polarity readout can be implemented with little or no increase in scan time by exploiting an observed b-value dependence and signal averaging. The b-value dependence was confirmed in healthy volunteers with distinct ghosting at low b-values but of negligible magnitude at b = 1000 s/mm2.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
School of Engineering and Computing, University of the West of Scotland, University of the West of Scotland - Paisley Campus, Paisley PA1 2BE, UK, City, Paisley, PA1 2BE, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Cancer grade classification is a challenging task identified from the cell structure of healthy and abnormal tissues. The partitioner learns about the malignant cell through the grading and plans the treatment strategy accordingly. A major portion of researchers used DL models for grade classification.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
Department of Ophthalmology, Hospital Universitario de Canarias, Carretera Ofra S/N, La Laguna, Santa Cruz de Tenerife, 38320, SPAIN.
This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
Shandong University of Traditional Chinese Medicine, Qingdao Academy of Chinese Medical Sciences, Jinan, Shandong, 250355, CHINA.
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease, and it can be used as an important indicator of disease progression. However, many existing methods focus mainly on the image itself when processing brain imaging data, ignoring other non-imaging data (e.g.
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