A COMPARISON OF THE EFFICACY OF DIAGNOSTIC IMAGING MODALITIES IN DETECTING COVID-19
Bibliographic record
Abstract
AIMS As COVID-19 continues to spread globally, the urgency for effective diagnostic testing escalates. Medical imaging has revolutionized healthcare as a critical step to early diagnosis, leading to immediate isolation and optimized treatment pathways. Current imaging modalities such as the lung ultrasound, chest X-ray, and computed tomography (CT) scan are critical in COVID-19 detection. However, overlap in clinical characteristics with other viral respiratory illnesses poses a significant risk for misdiagnosis. METHODOLOGY To address the need for information concerning the effectiveness of Coronavirus disease 2019 (COVID-19) diagnostic procedures, this paper reviews different imaging modalities, evaluating various factors including sensitivity, specificity, cost, diagnosis time, accessibility, safety, ease of implementation, and potential for optimization. The literature search reviewed databases including PubMed, Google Scholar, Sonography Canada, Cochrane Review, and Novanet using keywords to filter results. A utilitarian approach was employed to further refine selection criteria and to assess credibility and relevance. Applicable data was then extracted from literature for analysis considering the relationships between studies. RESULTS AND CONCLUSIONS The imaging modalities reviewed in this paper each have unique advantages. The lung ultrasound, with moderate sensitivity, permits regular monitoring due to its accessibility. Chest X-rays are effective at processing detailed images of the lungs to detect abnormalities but are not confirmed as a precise method for diagnosis. While CT scans show morphological features of COVID-19 with superior sensitivity, it is incapable of accurately differentiating coronavirus from other pulmonary diseases. Overall, a multimodality approach would be most effective for COVID-19 diagnosis and monitoring, preventing over dependence on CT scans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".