Adamsengupta9740

This paper aims at proposing a new machine-learning based model to improve the calculation of mealtime insulin boluses (MIB) in type 1 diabetes (T1D) therapy using continuous glucose monitoring (CGM) data. Indeed, MIB is still often computed through the standard formula (SF), which does not account for glucose rate-of-change ( ∆G), causing critical hypo/hyperglycemic episodes.

Four candidate models for MIB calculation, based on multiple linear regression (MLR) and least absolute shrinkage and selection operator (LASSO) are developed. The proposed models are assessed in silico, using the UVa/Padova T1D simulator, in different mealtime scenarios and compared to the SF and three ∆G-accounting variants proposed in the literature. An assessment on real data, by retrospectively analyzing 218 glycemic traces, is also performed.

All four tested models performed better than the existing techniques. LASSO regression with extended feature-set including quadratic terms (LASSO

) produced the best results. In silico, LASSO

reduced the error in estimating the optimal bolus to only 0.86U (1.45U of SF and 1.36-1.44U of literature methods), as well as hypoglycemia incidence (from 44.41% of SF and 44.60-45.01% of literature methods, to 35.93%). Results are confirmed by the retrospective application to real data.

New models to improve MIB calculation accounting for CGM- ∆G and easy-to-measure features can be developed within a machine learning framework. Particularly, in this paper, a new LASSO

model was developed, which ensures better glycemic control than SF and other literature methods.

MIB dosage with the proposed LASSO

model can potentially reduce the risk of adverse events in T1D therapy.

MIB dosage with the proposed LASSO Q model can potentially reduce the risk of adverse events in T1D therapy.

To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data.

Experimental data were obtained from two torso-tank setups in which Langendorff-perfused hearts (n = 4) were suspended and potentials recorded simultaneously from torso and epicardial surfaces. 49 different signal processing methods were applied to torso potentials, grouped as i) high-frequency noise removal (HFR) methods ii) baseline drift removal (BDR) methods and iii) combined HFR+BDR. The inverse problem was solved and reconstructed electrograms and activation maps compared to those directly recorded.

HFR showed no difference compared to not filtering in terms of absolute differences in reconstructed electrogram amplitudes nor median correlation in QRS waveforms (p>0.05). However, correlation and mean absolute error of activation times and pacing site localization were improved with all methods except a notch filter. HFR applieding the isoelectric point) is sufficient to see these improvements. HFR does not impact electrogram accuracy, but does impact post-processing to extract features such as activation times. Removal of line noise is insufficient to see these changes. HFR should be applied post-reconstruction to ensure over-filtering does not occur.Magnetic resonance electrical properties tomography (MR-EPT) maps the spatial distribution of the patient's electrical conductivity and permittivity using the measured B1 data in a magnetic resonance imaging (MRI) system. Existing MR-EPT methods are usually not clinically accessible owing to their technical limits such as strong noise sensitivity. In this study, we develop a new MR-EPT method that re-expresses the involved differential equations (DEs) based on the divergence theorem. In comparison with traditional methods, the proposed method avoids the grid-wise computation of the second-order derivatives of B1+ , thereby improving the robustness against noise. Besides, for applications where the structural information can be determined in advance, EPs of a region of interest (ROI) can be calculated in a fast and efficient manner. The proposed method is firstly validated with numerical simulations, in which a three-block phantom and an anatomically accurate Duke Head model are used to evaluate the proposed method. Experiments on the 9.4T MRI system were then conducted to validate the simulations. Both results indicated that the proposed MR-EPT solution could provide a more robust reconstruction of electrical properties maps compared with conventional methods.

Venous air embolism as a complication of contrast media administration from power injection systems in CT is found to occur in 7%-55% of patients, impacting patient safety, diagnostic image quality, workflow efficiency, and patient and radiographer satisfaction. learn more This study reviews the challenges associated with reactive air management approaches employed on contemporary systems, proposes a novel air management approach using proactive methods, and compares the impact of reactive and proactive approaches on injected air volumes under simulated clinical use.

Injected air volumes from three power injection systems were measured under simulated clinical use via custom air trap fixture. Two of the systems employed reactive air management approaches, while a new system implemented the proposed proactive air management approach.

The proactive system injected significantly less air (average of 0.005mL ± 0.006mL with a maximum of 0.017mL) when compared to two systems with reactive approaches (averages of 0.130mL ± 0.082mL and 0.106mL ± 0.094mL with maximums of 0.259mL and 0.311mL, respectively) (p < 0.05). CT images were taken of static and dynamic 0.1mL air bubbles inside of a vascular phantom, both of which were clearly visible. Additionally, the dynamic bubble was shown to introduce image artifacts similar to those observed clinically.

Comparison of the injected air volumes show that a system with a proactive air management approach injected significantly less air compared to tested systems employing reactive approaches.

The results indicate that the use of a proactive approach could significantly reduce the prevalence of observable, and potentially artifact-inducing, venous air embolism in contrast-enhanced CT procedures.

The results indicate that the use of a proactive approach could significantly reduce the prevalence of observable, and potentially artifact-inducing, venous air embolism in contrast-enhanced CT procedures.