Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema

Autor: Brusasco, C., Santori, G., Tavazzi, G., Via, G., Robba, C., Gargani, L., Mojoli, F., Mongodi, S., Bruzzo, E., Tro, R., Boccacci, P., Isirdi, A., Forfori, F., Corradi, F., Biagini, R., Calvaruso, A., Costanzo, D., Cundari, F., Della Rocca, A., Laffi, A., Marchi, E., Marino, F., Monfroni, M., Mongiovi, N., Narteni, S., Piagnani, C., Prizio, S., Protsyak, L., Romani, M., Soldati, I., Taddei, E., Tecchi, L., Trunfio, D.
Přispěvatelé: University of Zurich, Corradi, Francesco
Rok vydání: 2020
Předmět:
Artificial intelligence
medicine.medical_specialty
ARDS
Critical Illness
Lung ultrasonography
Pulmonary Edema
Heart failure
610 Medicine & health
Health Informatics
Acute respiratory failure
Critical Care and Intensive Care Medicine
11171 Cardiocentro Ticino
law.invention
03 medical and health sciences
0302 clinical medicine
law
Statistical significance
Anesthesiology
Computer aided diagnosis
Quantitative lung ultrasonography
Humans
Medicine
Lung
2718 Health Informatics
Respiratory Distress Syndrome
business.industry
Ultrasound
030208 emergency & critical care medicine
medicine.disease
Intensive care unit
Anesthesiology and Pain Medicine
030228 respiratory system
Computer-aided diagnosis
Extravascular Lung Water
2703 Anesthesiology and Pain Medicine
Radiology
Differential diagnosis
2706 Critical Care and Intensive Care Medicine
business
Zdroj: Journal of Clinical Monitoring and Computing. 36:131-140
ISSN: 1573-2614
1387-1307
Popis: Discriminating acute respiratory distress syndrome (ARDS) from acute cardiogenic pulmonary edema (CPE) may be challenging in critically ill patients. Aim of this study was to investigate if gray-level co-occurrence matrix (GLCM) analysis of lung ultrasound (LUS) images can differentiate ARDS from CPE. The study population consisted of critically ill patients admitted to intensive care unit (ICU) with acute respiratory failure and submitted to LUS and extravascular lung water monitoring, and of a healthy control group (HCG). A digital analysis of pleural line and subpleural space, based on the GLCM with second order statistical texture analysis, was tested. We prospectively evaluated 47 subjects: 16 with a clinical diagnosis of CPE, 8 of ARDS, and 23 healthy subjects. By comparing ARDS and CPE patients’ subgroups with HCG, the one-way ANOVA models found a statistical significance in 9 out of 11 GLCM textural features. Post-hoc pairwise comparisons found statistical significance within each matrix feature for ARDS vs. CPE and CPE vs. HCG (P ≤ 0.001 for all). For ARDS vs. HCG a statistical significance occurred only in two matrix features (correlation: P = 0.005; homogeneity: P = 0.048). The quantitative method proposed has shown high diagnostic accuracy in differentiating normal lung from ARDS or CPE, and good diagnostic accuracy in differentiating CPE and ARDS. Gray-level co-occurrence matrix analysis of LUS images has the potential to aid pulmonary edemas differential diagnosis.
Databáze: OpenAIRE
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