Suttonbitsch2434

The developed tool is evaluated by the 10-fold cross validation technique. Our findings suggest that the developed system is useful for diagnostic computational intelligence tool in hospital settings, and that it enables the automatic classification of HPT versus normal ECG signals. A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of multiple radiologists was investigated. 93 mass-like lesions were extensively augmented and used to train the network (n = 58 masses), which was then tested (n = 35 masses) against manual ground truth of a qualified breast radiologist with experience in breast CT imaging using the Conformity coefficient (with a value equal to 1 indicating a perfect performance). buy UCL-TRO-1938 Stability and diagnostic power of 672 radiomic descriptors were investigated between the computerized segmentation, and 4 radiologists' annotations for the 35 test set cases. Feature stability and diagnostic performance in the discrimination between benign and malignant cases were quantified using intraclass correlation (ICC) and multivariate analysis of variance (MANOVA), performed for each segmentation case (4 radiologists and DL algorithm). DL-based segmentation resulted in a Conformity of 0.85 ± 0.06 against the annotated ground truth. For the stability analysis, although modest agreement was found among the four annotations performed by radiologists (Conformity 0.78 ± 0.03), over 90% of all radiomic features were found to be stable (ICC>0.75) across multiple segmentations. All MANOVA analyses were statistically significant (p ≤ 0.05), with all dimensions equal to 1, and Wilks' lambda ≤0.35. In conclusion, DL-based mass segmentation in dedicated breast CT images can achieve high segmentation performance, and demonstrated to provide stable radiomic descriptors with comparable discriminative power in the classification of benign and malignant tumors to expert radiologist annotation. In the present study, we investigated the velocity profile over the carotid bifurcation in ten healthy volunteers by combining velocity measurements from two imaging modalities (PC-MRI and US-Doppler) and hemodynamic modeling in order to determine the optimal combination for the most realistic velocity estimation. The workflow includes data acquisition, velocity profile extraction at three sites (CCA, ECA and ICA), the arterial geometrical model reconstruction, a mesh generation and a rheological modeling. The results showed that US-Doppler measurements yielded higher velocity values as compared to PC-MRI (about 26% shift in CCA, 52% in ECA and 53% in ICA). This implies higher simulated velocities based on US-Doppler inlet as compared to simulated velocities based on PC-MRI inlet. Overall, PC-MRI inlet based simulations are closer to measurements than US-Doppler inlet based simulations. Moreover, the measured velocities showed that blood flow keeps a parabolic sectional profile distal from CCA, ECA and ICA, while being quite disturbed in the carotid sinus with a significant decrease in magnitude making this site very prone to atherosclerosis. BACKGROUND Partial arterial pressure of carbon dioxide (CO2) modulates cerebral blood flow through a vasoreactivity mechanism. Near infrared spectroscopy (NIRS) can be used to record these changes in cerebral hemodynamics. However, no laterality comparison of the NIRS signal has been performed despite being a prerequisite for the use of such a method in a vasoreactivity monitoring context. We propose to investigate the NIRS signal laterality in response to a CO2-inhalation-based hypercapnia paradigm in healthy volunteers. METHODS Eleven healthy volunteers (6 women, 5 men, mean age 31 ± 11) underwent a 3-block-design inhalation paradigm normoxia (5min, "baseline") - hypercapnia (2min, "stimulation") - normoxia (5min, "post-stimulation"). NIRS signal was measured using a two-channel oximeter (INVOS 5100C, Medtronic, USA) with sensors placed symmetrically on both left and right sides on each subject's forehead. Additional heart rate (HR) monitoring was performed simultaneously. Based on the NIRS mean signal pattern, an a priori model of parametric identification was applied for each channel to quantify parameters of interest (amplitude, time delay, excitation and post-stimulation time) for each inhalation block. RESULTS HR increased significantly during the stimulation block. The quality of the model was satisfactory mean absolute errors between modeled and experimental signals were lower than the resolution of the device. No significant lateralization was found between left and right values of most of the parameters. CONCLUSION Due to the lack of lateralization, this parametric identification of NIRS responses to hypercapnia could bring light to a potential asymmetry and be used as a biomarker in patients with cerebrovascular diseases. CONTEXT Determining which patients are ready for discharge from an Intensive Care Unit (ICU) presents a huge challenge, as ICU readmissions are associated with several negative outcomes such as increased mortality, length of stay, and cost compared to those patients who are not readmitted during their hospital stay. For these reasons, enhancing risk stratification in order to identify patients at high risk of clinical deterioration might benefit and improve the outcomes of critically ill hospitalized patients. Existing work on predicting ICU readmissions relies on information available at the time of discharge, however, in order to be more useful and to prevent complications, predictions need to be made earlier. GOALS In this work, we investigate the hypothesis that the basal characteristics and information collected at the time of the patient's admission can enable accurate predictions of ICU readmission. MATERIALS AND METHODS We analyzed an anonymized dataset of 11,805 adult patients from three ICUs in a Brrmore, our AUROC score of 0.91 (95% CI [0.89,0.92]) is higher than existing results published in the literature for other datasets. DISCUSSION AND CONCLUSION The results confirm our hypothesis. Our findings suggest that early markers can be used to anticipate patients at high risk of clinical deterioration after ICU discharge.