Possibilities of radiomics in processing data of CT scan of the chest organs in diagnosis of pulmonary tuberculosis
Bibliographic record
Abstract
The article shows the possibility of applying radiomics in the processing of chest CT data in the diagnosis of pulmonary tuberculosis. Currently, a subjective method based on the knowledge and experience of a radiologist is used to process CT images. A new approach to CT image analysis can fundamentally change the diagnostic process. Its essence is to create mathematical models and computer algorithms that take medical images as input and produce pathophysiological features of tissues.Dragonfly software, provided free of charge by OBYECT RESERCH SYSTEMS (ORS), Montreal, Canada, is used for CT slice analysis, which enables segmentation, mathematical and statistical processing of images, construction of ordinary and segmented histograms. To work with the program, dicom - CT files are transformed into raster files (Tiff, Jpeg, Raw) and further analysis of CT slices is performed by grayscale gradations (behind image pixels, not behind dicom file voxels). It should be emphasized that the grayscale analysis correlates with the Hounsfield units.It has been shown that based on the data of pathomorphological examination of the affected tissue, it is impossible to determine the difference between chemoresistant and susceptible pulmonary tuberculosis.Processing of CT data with the construction of conventional and segmental histograms using Dragonfly software tools makes it possible to identify pathophysiological features of tissues in the diagnosis of sensitive and chemoresistant pulmonary tuberculosis. Further research is needed to identify patterns and differences in the determination of densities in the diagnosis of sensitive and chemoresistant pulmonary tuberculosis.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".