Proof of Concept: Phantom Study to Ensure Quality and Safety of Portable Chest Radiography Through Glass During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Chest radiography is often used to detect lung involvement in patients with suspected pneumonia. Chest radiography through glass walls of an isolation room is a technique that could be immensely useful in the current COVID-19 pandemic. PURPOSE: The purpose of this study was to ensure quality and radiation safety while acquiring portable chest radiographs through the glass doors of isolation rooms using an adult anthropomorphic thorax phantom. MATERIALS AND METHODS: Sixteen chest radiographs were acquired utilizing different exposure factors without glass, through the smart glass, and through regular glass. Images were scored independently by 2 radiologists for quantum mottle and sharpness of anatomical structures using a 5-point Likert scale. Statistically significant differences in Likert scale scores and entrance surface dose (ESD) between images acquired without glass and through the smart and regular glass were tested. Interreader reliability was also evaluated. RESULTS: Compared with conventional radiography, equal or higher mean image quality scores (mottle and anatomical structures) were observed with the smart glass using 100 kVp at 12 mAs and 20 mAs and 125 kVp at 6.3 mAs (100 kVp at 2 mAs and 125 kVp at 3.2 mAs were used for conventional radiography observations). There was no statistically significant difference in the Likert scale scores for image quality and the entrance surface dose for radiographs acquired without glass, through the smart glass, and through regular glass. Backscatter from the smart glass was minimal at a distance of 3 m and was recorded as zero at a distance of 4 m from the x-ray tube outside an isolation room. CONCLUSIONS: Good-quality portable chest radiographs can be obtained safely through the smart glass doors of the isolation room. However, this technique does result in minor backscatter radiation. Modifications in the exposure factors (such as increasing milliampere seconds) may be required to optimize image quality while using this technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".