scholarly journals Multi‐institution consensus paper for acquisition of portable chest radiographs through glass barriers

2021 ◽  
Author(s):  
Sarah E. McKenney ◽  
John M. S. Wait ◽  
Virgil N. Cooper ◽  
Amirh M. Johnson ◽  
Jia Wang ◽  
...  
Keyword(s):  
Chest Radiographs ◽  
Consensus Paper ◽  
Portable Chest

ICU chest radiographs – ICU calamities: evaluation of the portable chest radiograph

2002 ◽  
Vol 9 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Sandra A. Oldham
Keyword(s):  
Chest Radiograph ◽  
Chest Radiographs ◽  
Portable Chest ◽  
Portable Chest Radiograph

scholarly journals Utility and efficacy of portable chest radiographs

1984 ◽  
Vol 142 (2) ◽  
pp. 265-267 ◽  
Author(s):  
ML Janower ◽  
Z Jennas-Nocera ◽  
J Mukai
Keyword(s):  
Chest Radiographs ◽  
Portable Chest

Routine portable chest radiographs in the medical intensive care unit

1997 ◽  
Vol 25 (5) ◽  
pp. 801-805 ◽  
Author(s):  
Ada Brainsky ◽  
Robert H. Fletcher ◽  
Henry A. Glick ◽  
Paul N. Lanken ◽  
Sankey V. Williams ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Medical Intensive Care Unit ◽  
Chest Radiographs ◽  
Portable Chest ◽  
Medical Intensive Care

scholarly journals Novel Method to Improve Radiologist Agreement in Interpretation of Serial Chest Radiographs in the ICU

2015 ◽  
Vol 5 ◽  
pp. 39 ◽  
Author(s):  
Denise A Castro ◽  
Asad A Naqvi ◽  
David Manson ◽  
Michael P Flavin ◽  
Elizabeth VanDenKerkhof ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Image Interpretation ◽  
Improve Patient Care ◽  
Chest Radiographs ◽  
The Other ◽  
Radiographic Appearance ◽  
Portable Chest ◽  
Novel Method ◽  
Research Ethics Board ◽  
Improve Patient

Objectives: To determine whether a novel method and device, called a variable attenuation plate (VAP), which equalizes chest radiographic appearance and allows for synchronization of manual image windowing with comparison studies, would improve consistency in interpretation. Materials and Methods: Research ethics board approved the prospective cohort pilot study, which included 50 patients in the intensive care unit (ICU) undergoing two serial chest radiographs with a VAP placed on each one of them. The VAP allowed for equalization of density and contrast between the patients’ serial chest radiographs. Three radiologists interpreted all the studies with and without the use of VAP. Kappa and percent agreement was used to calculate agreement between radiologists’ interpretations with and without the plate. Results: Radiologist agreement was substantially higher with the VAP method, as compared to that with the non-VAP method. Kappa values between Radiologists A and B, A and C, and B and C were 46%, 55%, and 51%, respectively, which improved to 73%, 81%, and 66%, respectively, with the use of VAP. Discrepant report impressions (i.e., one radiologist's impression of unchanged versus one or both of the other radiologists stating improved or worsened in their impression) ranged from 24 to 28.6% without the use of VAP and from 10 to 16% with the use of VAP (χ2 = 7.454, P < 0.01). Opposing views (i.e., one radiologist's impression of improved and one of the others stating disease progression or vice versa) were reported in 7 (12%) cases in the non-VAP group and 4 (7%) cases in the VAP group (χ2 = 0.85, P = 0.54). Conclusion: Numerous factors play a role in image acquisition and image quality, which can contribute to poor consistency and reliability of portable chest radiographic interpretations. Radiologists’ agreement of image interpretation can be improved by use of a novel method consisting of a VAP and associated software and has the potential to improve patient care.

scholarly journals Development of a digital duplication system for portable chest radiographs

1994 ◽  
Vol 7 (3) ◽  
pp. 146-153 ◽  
Author(s):  
Kenneth R. Hoffmann ◽  
Kunio Doi ◽  
Heber MacMahon ◽  
Maryellen L. Giger ◽  
Robert M. Nishikawa ◽  
...  
Keyword(s):  
Chest Radiographs ◽  
Portable Chest

scholarly journals Simple device to measure patient position on portable chest radiographs

1978 ◽  
Vol 131 (1) ◽  
pp. 169-170
Author(s):  
HE Gallant ◽  
PA Dietrich ◽  
T Shinozaki ◽  
RS Deane
Keyword(s):  
Chest Radiographs ◽  
Simple Device ◽  
Patient Position ◽  
Portable Chest

scholarly journals Social Distancing with Portable Chest Radiographs During the COVID-19 Pandemic: Assessment of Radiograph Technique and Image Quality Obtained at 6 Feet and Through Glass

2020 ◽  
Vol 2 (6) ◽  
pp. e200420
Author(s):  
Christopher P. Gange ◽  
Jay K. Pahade ◽  
Isabel Cortopassi ◽  
Anna S. Bader ◽  
Jamal Bokhari ◽  
...  
Keyword(s):  
Image Quality ◽  
Chest Radiographs ◽  
Social Distancing ◽  
Portable Chest

Receiver Operating Characteristic Evaluation of Computer Display of Adult Portable Chest Radiographs

1994 ◽  
Vol 29 (2) ◽  
pp. 141-146 ◽  
Author(s):  
ELIZABETH A. KRUPINSICI ◽  
KRIS MALONEY ◽  
STEVEN C. BESSEN ◽  
M PAUL CAPP ◽  
KENT GRAHAM ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Chest Radiographs ◽  
Portable Chest ◽  
Receiver Operating

scholarly journals Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units

2021 ◽  
Vol 8 ◽  
Author(s):  
Kumiko Tanaka ◽  
Taka-aki Nakada ◽  
Nozomi Takahashi ◽  
Takahiro Dozono ◽  
Yuichiro Yoshimura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Pleural Effusion ◽  
Diagnostic Accuracy ◽  
Intensive Care Units ◽  
Area Under The Curve ◽  
Chest Radiographs ◽  
Data Set ◽  
Portable Chest ◽  
Icu Physicians

Purpose: Portable chest radiographs are diagnostically indispensable in intensive care units (ICU). This study aimed to determine if the proposed machine learning technique increased in accuracy as the number of radiograph readings increased and if it was accurate in a clinical setting.Methods: Two independent data sets of portable chest radiographs (n = 380, a single Japanese hospital; n = 1,720, The National Institution of Health [NIH] ChestX-ray8 dataset) were analyzed. Each data set was divided training data and study data. Images were classified as atelectasis, pleural effusion, pneumonia, or no emergency. DenseNet-121, as a pre-trained deep convolutional neural network was used and ensemble learning was performed on the best-performing algorithms. Diagnostic accuracy and processing time were compared to those of ICU physicians.Results: In the single Japanese hospital data, the area under the curve (AUC) of diagnostic accuracy was 0.768. The area under the curve (AUC) of diagnostic accuracy significantly improved as the number of radiograph readings increased from 25 to 100% in the NIH data set. The AUC was higher than 0.9 for all categories toward the end of training with a large sample size. The time to complete 53 radiographs by machine learning was 70 times faster than the time taken by ICU physicians (9.66 s vs. 12 min). The diagnostic accuracy was higher by machine learning than by ICU physicians in most categories (atelectasis, AUC 0.744 vs. 0.555, P &lt; 0.05; pleural effusion, 0.856 vs. 0.706, P &lt; 0.01; pneumonia, 0.720 vs. 0.744, P = 0.88; no emergency, 0.751 vs. 0.698, P = 0.47).Conclusions: We developed an automatic detection system for portable chest radiographs in ICU setting; its performance was superior and quite faster than ICU physicians.

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