Using Quantitative Computed Tomographic Imaging to Understand Chronic Obstructive Pulmonary Disease and Fibrotic Interstitial Lung Disease
Bibliographic record
Abstract
Computed tomography (CT) is commonly used in the evaluation and management of patients with diffuse lung pathologies, including chronic obstructive pulmonary disease (COPD) and fibrotic interstitial lung disease (ILD). In clinical practice, the qualitative (visual) assessment of CT images by a radiologist provides insight into the diagnosis of diffuse lung disease, estimates disease severity, and supports the identification of complications. Quantitative CT (qCT) is an emerging technique that provides some advantages over qualitative assessment. qCT can allow early and accurate detection of emphysema and airway disease, as well as aiding the evaluation of disease burden in both COPD and ILD. This approach is starting to be used as a surrogate biomarker in clinical trials to assess response to therapy. Artificial intelligence techniques have recently been incorporated into qCT, with such rapid evolution that it is currently difficult to determine the exact role it will eventually play in evaluating patients with COPD or pulmonary fibrosis. This article reviews the current state of the art for qualitative and qCT assessment of both COPD and fibrotic ILD. Current areas of controversy and limitations of these techniques are discussed, along with the potential future role of artificial intelligence. Recommendations are provided with regard to the current use of these techniques in the management of patients with diffuse lung disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".