Determination of dynamics and stage of development of COVID-19 pneumonia based on digital software processing of images of computed tomography of the chest
Notice bibliographique
Résumé
BACKGROUND. Pneumonia resulting from SARS-CoV-2 infection is characterized by the development of certain radiological patterns such as “ground glass” and others, which can only be detected using high-resolution computed tomography (HRCT) of the chest. The use of CT to combat the coronavirus disease (COVID-19) pandemic varies worldwide. In scientific publications, there is no consensus on the information content of CT for COVID-19 pneumonia, the timing of its implementation at the onset of the disease and control examinations. The long-term consequences of the disease, the possibilities of software processing of CT images of the chest cavity in order to study the dynamics of the process, predict outcomes and evaluate the effectiveness of the therapy, have not been sufficiently studied. OBJECTIVE. To determine the possibilities of digital software processing to increase the information content of CT images and justify its use in determining the dynamics and stage of development of COVID-19 pneumonia. MATERIALS AND METHODS. For image analysis, we used the Dragonfly program, which was provided free of charge for non-commercial scientific research by Object Research Systems (Montreal, Canada). The program allows you to carry out segmentation, mathematical and statistical processing of images, to build conventional and segmented histograms. To use it, it is necessary to reformat DICOM files of CT into so-called raster files (TIFF, JPEG). Further analysis of images of CT slices is performed using gray scale (from 64 to 512 gradations), which correlates with density values on the Hounsfield scale, which allows you to more accurately determine the structure of the organ under study. RESULTS AND DISCUSSION. The analysis of CT for COVID-19 pneumonia with the “ground glass” pattern showed that standard densitometry does not allow to determine the morphofunctional difference between the studied areas of “ground glass” and to determine the stage of its development. The segmentation histogram based on digital software processing of the same “ground glass” areas of the patient with COVID-19 pneumonia has changed significantly with a clear definition of a significant difference in the density of the pattern in dynamics, on the basis of which it can be concluded that the patient is undergoing a process of recovery and COVID-19 pneumonia is at the resolution stage. CONCLUSIONS. Carrying out digital software image processing with a CT segmentation histogram allows you to determine the dynamics and stage of development of COVID-19 pneumonia, evaluate the effectiveness and need for therapeutic measures. In order to conduct an objective control of the inflammatory process in COVID-19 pneumonia, it is necessary to conduct a HRCT of the chest at discharge from the hospital, especially in patients who were treated in the intensive care unit. If there are significant changes, follow-up studies should be carried out after 3 and 6 months, or depending on clinical indications.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».