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Virsilas E, Liubsys A, Janulionis A, Valiulis A. Noninvasive Respiratory Support Effects on Sighs in Preterm Infants by Electrical Impedance Tomography. Indian J Pediatr 2022:10.1007/s12098-022-04413-8. [PMID: 36539568 DOI: 10.1007/s12098-022-04413-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 10/26/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE To evaluate differences regarding sigh frequency between noninvasive respiratory support types and to assess regional ventilation distribution, delta Z, and end-expiratory lung impedance differences before and after sighs. METHODS Very low-birth-weight infants with gestational ages less than 32 wk were included in the study. Participants were split into two groups: those receiving continuous positive airway pressure and infants receiving high-flow nasal cannula therapy. RESULTS The study enrolled 30 infants. The high-flow nasal cannula therapy group had more sighs per 10-min period than infants receiving continuous positive airway pressure (p = 0.016). Ventilation distribution was similar in the anterior and right ventilation distribution compartments pre- and post-sigh (46.30% vs. 45.68% and 54.27% vs. 55.26%, respectively). No statistically significant increase in end-expiratory lung impedance or delta Z was observed in global or separate lung regions (p > 0.05). CONCLUSION The study has demonstrated that sighs are more frequent in infants receiving high-flow nasal cannula respiratory support compared to continuous positive airway pressure. Spontaneously occurring sighs on noninvasive respiratory support due to respiratory distress syndrome (RDS) do not increase end-expiratory lung impedance or alter delta Z, and appear to have limited clinical significance. TRIAL REGISTRATION Prospectively registered at www. CLINICALTRIALS gov , reg. No. NCT04542096, reg. date 01/09/2020.
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Affiliation(s)
- Ernestas Virsilas
- Clinic of Children's Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Santariškių G. 7, 08406, Vilnius, Lithuania.
| | - Arunas Liubsys
- Clinic of Children's Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Santariškių G. 7, 08406, Vilnius, Lithuania
| | - Adomas Janulionis
- Clinic of Children's Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Santariškių G. 7, 08406, Vilnius, Lithuania
| | - Arunas Valiulis
- Clinic of Children's Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Santariškių G. 7, 08406, Vilnius, Lithuania
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Zhang T, Tian X, Liu X, Ye J, Fu F, Shi X, Liu R, Xu C. Advances of deep learning in electrical impedance tomography image reconstruction. Front Bioeng Biotechnol 2022; 10:1019531. [PMID: 36588934 PMCID: PMC9794741 DOI: 10.3389/fbioe.2022.1019531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.
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Affiliation(s)
- Tao Zhang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China,Drug and Instrument Supervision and Inspection Station, Xining Joint Logistics Support Center, Lanzhou, China
| | - Xiang Tian
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - XueChao Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - JianAn Ye
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Feng Fu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - XueTao Shi
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - RuiGang Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - CanHua Xu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China,*Correspondence: CanHua Xu,
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Gao L, Zhu Y, Pan C, Yin Y, Zhao Z, Yang L, Zhang J. A randomised trial evaluating mask ventilation using electrical impedance tomography during anesthetic induction: one-handed technique versus two-handed technique. Physiol Meas 2022; 43. [PMID: 35580595 DOI: 10.1088/1361-6579/ac70a3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/17/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Mask positive pressure ventilation could lead to inhomogeneity of lung ventilation, potentially inducing lung function impairments, when compared with spontaneous breathing. The inhomogeneity of lung ventilation can be monitored by chest electrical impedance tomography (EIT), which could increase our understanding of mask ventilation-derived respiratory mechanics. We hypothesized that two-handed mask holding ventilation technique had better lung ventilation reflected by respiratory mechanics when compared with one-handed mask holding technique. APPROACH Elective surgical patients with healthy lungs were randomly assigned to receive either one-handed mask holding (one-handed group) or two-handed mask holding (two-handed group) ventilation. Mask ventilation was performed by certified registered anesthesiologists, during which the patients were mechanically ventilated with pressure-controlled mode. EIT was used to assess respiratory mechanics including: ventilation distribution, global and regional respiratory system compliance (CRS), expiratory tidal volume (TVe) and minute ventilation volume. Besides, hemodynamic parameters and PaO2-FiO2-ratio were also recorded. MAIN RESULTS Eighty adult patients were included in this study. Compared with spontaneous ventilation, mask positive pressure ventilation caused inhomogeneity of lung ventilation in both one-handed group (global inhomogeneity index: 0.40±0.07 vs. 0.50±0.15; P<0.001) and two-handed group (0.40±0.08 vs. 0.50±0.13; P<0.001). There were no differences of global inhomogeneity index (P = 0.948) between the one-handed group and two-handed group. Compared with one-handed group, two-handed group was associated with higher TVe (552.6±184.2 ml vs. 672.9±156.6 ml, P=0.002) and higher global CRS (46.5±16.4 ml/cmH2O vs. 53.5±14.5 ml/cmH2O, P=0.049). No difference of PaO2-FiO2-ratio was found between two groups (P=0.743). SIGNIFICANCE The two-handed mask holding technique could not improve the inhomogeneity of lung ventilation when monitored by EIT during mask ventilation although it obtained larger expiratory tidal volumes than one-handed mask holding technique.
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Affiliation(s)
- Lingling Gao
- Fudan University Shanghai Cancer Center, 270 Dong An Road, Xuhui, Shanghai, 200032, China, Shanghai, Shanghai, 200032, CHINA
| | - Yun Zhu
- Fudan University Shanghai Cancer Center, 270 Dong An Road, Xuhui, Shanghai, 200032, China, Shanghai, Shanghai, 200032, CHINA
| | - Congxia Pan
- Fudan University Shanghai Cancer Center, 270 Dong An Road, Xuhui, Shanghai, 200032, China, Shanghai, Shanghai, 200032, CHINA
| | - Yuehao Yin
- Fudan University Shanghai Cancer Center, 270 Dong An Road, Xuhui, Shanghai, 200032, China, Shanghai, Shanghai, 200032, CHINA
| | - Zhanqi Zhao
- Department of Biomedical Engineering, Fourth Military Medical University, Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China, Xi'an, 710032, CHINA
| | - Li Yang
- Fudan University Shanghai Cancer Center, Department of Anesthesiology, Fudan University Shanghai Cancer Center, 270 Dong An Road, Xuhui, Shanghai, 200032, China, Shanghai, 200032, CHINA
| | - Jun Zhang
- Fudan University Shanghai Cancer Center, 270 Dong An Road, Xuhui, Shanghai, 200032, China, Shanghai, Shanghai, 200032, CHINA
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