| 1. |
- Chiesa, Scott T, et al.
(författare)
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Determinants of Intima-Media Thicknessin the Young: The ALSPAC Study.
- 2021
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Ingår i: JACC. Cardiovascular imaging. - : Elsevier BV. - 1876-7591 .- 1936-878X. ; 14:2, s. 468-478
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Tidskriftsartikel (refereegranskat)abstract
- This study characterized the determinants of carotid intima-media thickness (cIMT) in a large (n > 4,000) longitudinal cohort of healthy young people age 9 to 21 years.Greater cIMT is commonly used in the young as a marker of subclinical atherosclerosis, but its evolution at this age is still poorly understood.Associations between cardiovascular risk factors and cIMT were investigated in both longitudinal (ages 9 to 17 years) and cross-sectional (ages 17 and 21 years) analyses, with the latter also related to other measures of carotid structure and stress. Additional use of ultra-high frequency ultrasound in the radial artery at age 21 years allowed investigation of the distinct layers (i.e., intima or media) that may underlie observed differences.Fat-free mass (FFM) and systolic blood pressure were the only modifiable risk factors positively associated with cIMT (e.g., mean difference in cIMT per 1-SD increase in FFM at age 17: 0.007mm: 95% confidence interval [CI]: 0.004 to 0.010; p<0.001), whereas fat mass was negatively associated with cIMT (difference:-0.0032; 95%CI: 0.004 to-0.001; p=0.001). Similar results were obtained when investigating cumulative exposure to these factors throughout adolescence. An increase in cIMT maintained circumferential wall stress in the face of increased mean arterial pressure when increases in body mass were attributable to increased FFM, but not fat mass. Risk factor-associated differences in the radial artery occurred in the media alone, and there was little evidence of a relationship between intimal thickness and any risk factor.Subtle changes in cIMT in the young may predominantly involve the media and represent physiological adaptations as opposed to subclinical atherosclerosis. Other vascular measures might be more appropriate for the identification of arterial disease before adulthood.
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| 2. |
- Jaubert, Olivier, et al.
(författare)
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Training deep learning based dynamic MR image reconstruction using open-source natural videos
- 2024
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Ingår i: Scientific Reports. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian, radial, spiral) either from true multi-coil cardiac MR data (N = 692) or from synthetic MR data simulated from Inter4K natural videos (N = 588). Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS). Differences were assessed in simulations (N = 104 datasets) in terms of MSE, PSNR, and SSIM and prospectively for cardiac cine (short axis, four chambers, N = 20) and speech cine (N = 10) data in terms of subjective image quality ranking, SNR and Edge sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were performed to assess statistical significance. In simulated data, DL networks trained with cardiac data outperformed DL networks trained with natural videos, both of which outperformed CS (p < 0.05). However, in prospective experiments DL reconstructions using both training datasets were ranked similarly (and higher than CS) and presented no statistical differences in SNR and Edge Sharpness for most conditions.The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). The natural video dataset, code and pre-trained networks are made readily available on github.
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| 3. |
- Naci, Huseyin, et al.
(författare)
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Impact of predictive medicine on therapeutic decision making : a randomized controlled trial in congenital heart disease
- 2019
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Ingår i: npj Digital Medicine. - : Springer Science and Business Media LLC. - 2398-6352. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- Computational modelling has made significant progress towards clinical application in recent years. In addition to providing detailed diagnostic data, these methods have the potential to simulate patient-specific interventions and to predict their outcome. Our objective was to evaluate to which extent patient-specific modelling influences treatment decisions in coarctation of the aorta (CoA), a common congenital heart disease. We selected three cases with CoA, two of which had borderline indications for intervention according to current clinical guidelines. The third case was not indicated for intervention according to guidelines. For each case, we generated two separate datasets. First dataset included conventional diagnostic parameters (echocardiography and magnetic resonance imaging). In the second, we added modelled parameters (pressure fields). For the two cases with borderline indications for intervention, the second dataset also included pressure fields after virtual stenting simulations. All parameters were computed by modelling methods that were previously validated. In an online-administered, invitation-only survey, we randomized 178 paediatric cardiologists to view either conventional (control) or add-on modelling (experimental) datasets. Primary endpoint was the proportion of participants recommending different therapeutic options: (1) surgery or catheter lab (collectively, intervention) or (2) no intervention (follow-up with or without medication). Availability of data from computational predictive modelling influenced therapeutic decision making in two of three cases. There was a statistically significant association between group assignment and the recommendation of an intervention for one borderline case and one non-borderline case: 94.3% vs. 72.2% (RR: 1.31, 95% CI: 1.14-1.50, p = 0.00) and 18.8% vs. 5.1% (RR: 3.09, 95% CI: 1.17-8.18, p = 0.01) of participants in the experimental and control groups respectively recommended an intervention. For the remaining case, there was no difference between the experimental and control group and the majority of participants recommended intervention. In sub-group analyses, findings were not affected by the experience level of participating cardiologists. Despite existing clinical guidelines, the therapy recommendations of the participating physicians were heterogeneous. Validated patient-specific computational modelling has the potential to influence treatment decisions. Future studies in broader areas are needed to evaluate whether differences in decisions result in improved outcomes.
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