While sustainability is at the centre of many government agendas, there is a great risk of entrusting strategic decisions to those lacking in sustainability expertise. It is therefore necessary to ...ensure that universities are the green engines of sustainable communities. The present study administered a questionnaire to students enrolled in a Management Engineering programme at an Italian university, to collect their perceptions of and opinions on sustainability and energy issues. Students completed the questionnaire twice: once prior to beginning and once at the end of term. The results showed that students held more sustainable attitudes at the end of term, and perceived sustainable education and youth confidence as the building blocks of future society. They also observed that decarbonisation of the Italian energy system and national energy independence would require the significant development of renewable systems and interventions to promote energy efficiency. In addition, they recognised subsidies for green production, energy communities, differentiated waste collection and professional skills training as crucial. The sustainable university should support younger generations by encouraging student engagement in real-world projects and the development of long-term, structured teacher-student relationships.
Higher education institutions (HEIs), based on learning, innovation, and research, can support the progress of civil society. Many HEIs are implementing sustainability practices and projects to ...counteract climate change, often involving youth participation. The present study aimed at identifying how sustainable communities may be fostered in a university setting. To that end, a questionnaire was administered to engineering students at the start and end of a course on energy issues, assessing their perceptions of sustainability using multi-criteria decision analysis. The results showed that students placed greater value on sustainability at the end of the course. Additionally, the findings highlight that the implementation of projects aimed at tackling real problems may be useful for disseminating knowledge and sustainable practices. The main implications of this study indicate that sustainable communities in academia lay on six foundational pillars: sustainable education, energy (and resource) independence, subsidies in support of the green economy, initiatives aimed at reducing the carbon footprint, energy community development, and new green professional opportunities.
Artificial intelligence (AI) is the development of computer systems whereby machines can mimic human actions. This is increasingly used as an assistive tool to help clinicians diagnose and treat ...diseases. Periodontitis is one of the most common diseases worldwide, causing the destruction and loss of the supporting tissues of the teeth. This study aims to assess current literature describing the effect AI has on the diagnosis and epidemiology of this disease. Extensive searches were performed in April 2022, including studies where AI was employed as the independent variable in the assessment, diagnosis, or treatment of patients with periodontitis. A total of 401 articles were identified for abstract screening after duplicates were removed. In total, 293 texts were excluded, leaving 108 for full-text assessment with 50 included for final synthesis. A broad selection of articles was included, with the majority using visual imaging as the input data field, where the mean number of utilised images was 1666 (median 499). There has been a marked increase in the number of studies published in this field over the last decade. However, reporting outcomes remains heterogeneous because of the variety of statistical tests available for analysis. Efforts should be made to standardise methodologies and reporting in order to ensure that meaningful comparisons can be drawn.
Respiratory diseases are leading causes of mortality and morbidity worldwide. Pulmonary imaging is an essential component of the diagnosis, treatment planning, monitoring, and treatment assessment of ...respiratory diseases. Insights into numerous pulmonary pathologies can be gleaned from functional lung MRI techniques. These include hyperpolarized gas ventilation MRI, which enables visualization and quantification of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. Here, we evaluate several 3D convolutional neural networks to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 759 helium-3 (
He) or xenon-129 (
Xe) volumetric scans and corresponding expert segmentations from 341 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined
He and
Xe MRI scans using a 3D nn-UNet outperformed other DL methods, achieving a mean ± SD Dice coefficient of 0.963 ± 0.018, average boundary Hausdorff distance of 1.505 ± 0.969 mm, Hausdorff 95th percentile of 5.754 ± 6.621 mm and relative error of 0.075 ± 0.039. Moreover, limited differences in performance were observed between
Xe and
He scans in the testing set. Combined training on
Xe and
He yielded statistically significant improvements over the conventional methods (p < 0.0001). In addition, we observed very strong correlation and agreement between DL and expert segmentations, with Pearson correlation of 0.99 (p < 0.0001) and Bland-Altman bias of - 0.8%. The DL approach evaluated provides accurate, robust and rapid segmentations of ventilated lung regions and successfully excludes non-lung regions such as the airways and artefacts. This approach is expected to eliminate the need for, or significantly reduce, subsequent time-consuming manual editing.
Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized ...equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (
H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural
H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory
H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional
H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping.
Hyperpolarised helium-3 (
He) ventilation magnetic resonance imaging (MRI) and multiple-breath washout (MBW) are sensitive methods for detecting lung disease in cystic fibrosis (CF). We aimed to ...explore their relationship across a broad range of CF disease severity and patient age, as well as assess the effect of inhaled lung volume on ventilation distribution.32 children and adults with CF underwent MBW and
He-MRI at a lung volume of end-inspiratory tidal volume (EI
). In addition, 28 patients performed
He-MRI at total lung capacity.
He-MRI scans were quantitatively analysed for ventilation defect percentage (VDP), ventilation heterogeneity index (VHI) and the number and size of individual contiguous ventilation defects. From MBW, the lung clearance index, convection-dependent ventilation heterogeneity (Scond) and convection-diffusion-dependent ventilation heterogeneity (Sacin) were calculated.VDP and VHI at EI
strongly correlated with lung clearance index (r=0.89 and r=0.88, respectively), Sacin (r=0.84 and r=0.82, respectively) and forced expiratory volume in 1 s (FEV
) (r=-0.79 and r=-0.78, respectively). Two distinct
He-MRI patterns were highlighted: patients with abnormal FEV
had significantly (p<0.001) larger, but fewer, contiguous defects than those with normal FEV
, who tended to have numerous small volume defects. These two MRI patterns were delineated by a VDP of ∼10%. At total lung capacity, when compared to EI
, VDP and VHI reduced in all subjects (p<0.001), demonstrating improved ventilation distribution and regions of volume-reversible and nonreversible ventilation abnormalities.
Background
Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially ...co‐registered functional hyperpolarized gas and structural proton (1H)‐MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single‐channel, mono‐modal deep learning (DL)‐based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single‐channel alternatives.
Purpose
We hypothesized that a DL‐based dual‐channel approach, leveraging both 1H‐MRI and Xenon‐129‐MRI (129Xe‐MRI), can generate LCEs more accurately than single‐channel alternatives.
Study Type
Retrospective.
Population
A total of 480 corresponding 1H‐MRI and 129Xe‐MRI scans from 26 healthy participants (median age range: 11 8–71; 50% females) and 289 patients with pulmonary pathologies (median age range: 47 6–83; 51% females) were split into training (422 scans 88%; 257 participants 82%) and testing (58 scans 12%; 58 participants 18%) sets.
Field Strength/Sequence
1.5‐T, three‐dimensional (3D) spoiled gradient‐recalled 1H‐MRI and 3D steady‐state free‐precession 129Xe‐MRI.
Assessment
We developed a multimodal DL approach, integrating 129Xe‐MRI and 1H‐MRI, in a dual‐channel convolutional neural network. We compared this approach to single‐channel alternatives using manually edited LCEs as a benchmark. We further assessed a fully automatic DL‐based framework to calculate VDPs and compared it to manually generated VDPs.
Statistical Tests
Friedman tests with post hoc Bonferroni correction for multiple comparisons compared single‐channel and dual‐channel DL approaches using Dice similarity coefficient (DSC), average boundary Hausdorff distance (average HD), and relative error (XOR) metrics. Bland–Altman analysis and paired t‐tests compared manual and DL‐generated VDPs. A P value < 0.05 was considered statistically significant.
Results
The dual‐channel approach significantly outperformed single‐channel approaches, achieving a median (range) DSC, average HD, and XOR of 0.967 (0.867–0.978), 1.68 mm (37.0–0.778), and 0.066 (0.246–0.045), respectively. DL‐generated VDPs were statistically indistinguishable from manually generated VDPs (P = 0.710).
Data Conclusion
Our dual‐channel approach generated LCEs, which could be integrated with ventilated lung segmentations to produce biomarkers such as the VDP without manual intervention.
Evidence Level
4.
Technical Efficacy
Stage 1.
Preterm birth is associated with low lung function in childhood, but little is known about the lung microstructure in childhood.
We assessed the differential associations between the historical ...diagnosis of bronchopulmonary dysplasia (BPD) and current lung function phenotypes on lung ventilation and microstructure in preterm-born children using hyperpolarized
Xe ventilation and diffusion-weighted magnetic resonance imaging (MRI) and multiple-breath washout (MBW).
Data were available from 63 children (aged 9-13 yr), including 44 born preterm (⩽34 weeks' gestation) and 19 term-born control subjects (⩾37 weeks' gestation). Preterm-born children were classified, using spirometry, as prematurity-associated obstructive lung disease (POLD; FEV
< lower limit of normal LLN and FEV
/FVC < LLN), prematurity-associated preserved ratio of impaired spirometry (FEV
< LLN and FEV
/FVC ⩾ LLN), preterm-(FEV
⩾ LLN) and term-born control subjects, and those with and without BPD. Ventilation heterogeneity metrics were derived from
Xe ventilation MRI and SF
MBW. Alveolar microstructural dimensions were derived from
Xe diffusion-weighted MRI.
Xe ventilation defect percentage and ventilation heterogeneity index were significantly increased in preterm-born children with POLD. In contrast, mean
Xe apparent diffusion coefficient,
Xe apparent diffusion coefficient interquartile range, and
Xe mean alveolar dimension interquartile range were significantly increased in preterm-born children with BPD, suggesting changes of alveolar dimensions. MBW metrics were all significantly increased in the POLD group compared with preterm- and term-born control subjects. Linear regression confirmed the differential effects of obstructive disease on ventilation defects and BPD on lung microstructure.
We show that ventilation abnormalities are associated with POLD, and BPD in infancy is associated with abnormal lung microstructure.
Chan et al examined the differential associations between the historical diagnosis of bronchopulmonary dysplasia (BPD) and current lung function phenotypes on lung ventilation and microstructure in ...preterm-born children using hyperpolarized Xe ventilation and diffusion-weighted magnetic resonance imaging (MRI) and multiple-breath washout (MBW). Xe ventilation defect percentage and ventilation heterogeneity index were significantly increased in preterm-born children with prematurity-associated obstructive lung disease (POLD). They show that ventilation abnormalities are associated with POLD, and BPD in infancy is associated with abnormal lung microstructure.
Free-breathing
H ventilation MRI shows promise but only single-center validation has yet been performed against methods which directly image lung ventilation in patients with cystic fibrosis (CF).
To ...investigate the relationship between
Xe and
H ventilation images using data acquired at two centers.
Sequence comparison.
Center 1; 24 patients with CF (12 female) aged 9-47 years. Center 2; 7 patients with CF (6 female) aged 13-18 years, and 6 healthy controls (6 female) aged 21-31 years. Data were acquired in different patients at each center.
1.5 T, 3D steady-state free precession and 2D spoiled gradient echo.
Subjects were scanned with
Xe ventilation and
H free-breathing MRI and performed pulmonary function tests. Ventilation defect percent (VDP) was calculated using linear binning and images were visually assessed by H.M., L.J.S., and G.J.C. (10, 5, and 8 years' experience).
Correlations and linear regression analyses were performed between
Xe VDP,
H VDP, FEV
, and LCI. Bland-Altman analysis of
Xe VDP and
H VDP was carried out. Differences in metrics were assessed using one-way ANOVA or Kruskal-Wallis tests.
Xe VDP and
H VDP correlated strongly with; each other (r = 0.84), FEV
z-score (
Xe VDP r = -0.83,
H VDP r = -0.80), and LCI (
Xe VDP r = 0.91,
H VDP r = 0.82). Bland-Altman analysis of
Xe VDP and
H VDP from both centers had a bias of 0.07% and limits of agreement of -16.1% and 16.2%. Linear regression relationships of VDP with FEV
were not significantly different between
Xe and
H VDP (P = 0.08), while
Xe VDP had a stronger relationship with LCI than
H VDP.
H ventilation MRI shows large-scale agreement with
Xe ventilation MRI in CF patients with established lung disease but may be less sensitive to subtle ventilation changes in patients with early-stage lung disease.
2 TECHNICAL EFFICACY: Stage 2.
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