Differential Diagnostics of the Disappearing Lung Syndrome in Lymphangioleiomyomatosis and COVID-19 Pneumonia Using Digital Software Processing of Computer Tomography Data (Clinical Cases)
Bibliographic record
Abstract
About 20 % of people who fell ill during the COVID-19 pandemic had a severe course of the disease, which was accompanied by various complications. One of these complications is the disappearing lung syndrome, which can be observed both in the acute period of the disease and in the post-COVID period. Under the mask of pulmonary complications of COVID-19, rare interstitial lung diseases may be diagnosed late. COVID-19 is characterised by the development of systemic thrombovasculitis against the background of a hyperimmune response caused by SARS-CoV-2. These pathological processes can lead to the formation of giant multicompartmental cystic cavities in the lungs, which are similar to those observed in lymphangioleiomyomatosis (LAM). Objective — to investigate the possibility of differential diagnosis of the disappearing lung syndrome in lung lymphangioleiomyomatosis and the complicated course of viral pneumonia COVID-19 using digital software processing of CT data Materials and methods. The data of CT lung of patients with LAM and patients with a complicated course of viral pneumonia COVID-19 were analyzed in dynamics. CT was performed on an Aquilion TSX-101A Tochiba scanner (Japan) with subsequent digital processing using the Dragonfly program, OBYECT RESEARCH SYSTEMS (ORS), Montreal, Canada, and comparison of the obtained results with pathomorphological changes. Examples of own observations are given. Results and discussion. Researched of changes in the structure of the lung parenchyma in cases of LAM and patients with a complicated course of nosocomial viral pneumonia of COVID-19 were studied by means of software digital processing of CT OGK data. The obtained results in the form of segmented histograms are correlated with pathomorphological changes in lung tissue.Digital software processing of CT data clearly reflects the morphological structure of the lung parenchyma and allows diagnosis and differential diagnosis of «disappearing lung syndrome» in various diseases. Conclusions. Carrying out digital software processing of CT OGK data allows differential diagnosis of various pathological processes, which are radiologically manifested by the same symptoms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".