Non-contiguous Computed Tomography Lung Scans Can be Manipulated to Permit Artificial Intelligence Analyses for Interstitial Lung Disease in Systemic Sclerosis
Bibliographic record
Abstract
Abstract Artificial intelligence (AI) can analyse high-resolution CT lung scans (HRCT) in various interstitial lung diseases (ILDs), including systemic sclerosis (SSc). Older HRCT lung scans may have been saved as small DICOM file sets consisting of non-contiguous slices. These are not amenable to AI analyses. Our aim was to develop and test a method of rebuilding small non-contiguous sets of HRCT lung slices into larger sets of contiguous slices that could be analysed by AI programs. We deleted sets of DICOM files from 14 large DICOM file set scans from SSc patients and were left with a scan with about 30 equidistant non-contiguous slices. We then inserted copies of scans between each pair of slices to create a large DICOM file set similar in size to the original large file set scan. Both the original scan and the rebuilt large DICOM file set scan were analysed by Contextflow ADVANCE Chest CT. We recorded the values for honeycombing (HC), reticular pattern (RP), ground glass opacities (GGO), and total ILD. We analysed agreement between the original scan and the rebuilt large DICOM file set scan using intraclass correlation coefficient (ICC), Lin’s concordance correlation coefficient (CCC), Bland–Altman limits-of-agreement (LOA) plots and the Bradley–Blackwood P-value. ICC, CCC, Bradly–Blackwood P-values and Bland–Altman plots showed excellent agreement between scans for HC, RC, GGO, and total ILD except for the Bradley–Blackwood P-value for RP. Non-contiguous HRCT small DICOM file set lung scans in SSc can be manipulated to allow analysis by AI.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".