Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, more similar to a clinical setting than what is typically provided in multiple sclerosis segmentation challenges. Furthermore, we evaluate a prototype naive combination of the two methods, which refines the final segmentation. All methods were trained on 32 patients, and the evaluation was performed on a pure test set of 73 cases. Results show low lesion-wise false positives (30%) for the deep learning architecture, whereas the shallow architecture yields the best Dice coefficient (63%) and volume difference (19%). Combining both shallow and deep architectures further improves the lesion-wise metrics (69% and 26% lesion-wise true and false positive rate, respectively).Comment: Accepted to the MICCAI 2018 Brain Lesion (BrainLes) worksho

Left and right ventricle assessment with Cardiac CT: validation study vs. Cardiac MR

Objectives To compare Magnetic Resonance (MR) and Computed Tomography (CT) for the assessment of left (LV) and right (RV) ventricular functional parameters. Methods Seventy nine patients underwent both Cardiac CT and Cardiac MR. Images were acquired using short axis (SAX) reconstructions for CT and 2D cine b-SSFP (balanced- steady state free precession) SAX sequence for MR, and evaluated using dedicated software. Results CT and MR images showed good agreement: LV EF (Ejection Fraction) (52±14% for CT vs. 52±14% for MR; r0 0.73; p>0.05); RV EF (47±12% for CT vs. 47±12% for MR; r00.74; p>0.05); LV EDV (End Diastolic Volume) (74± 21 ml/m 2 for CT vs. 76±25 ml/m 2 for MR; r00.59; p>0.05); RV EDV (84±25 ml/m 2 for CT vs. 80±23 ml/m 2 for MR; r0 0.58; p>0.05); LV ESV (End Systolic Volume)(37±19 ml/m 2 for CT vs. 38±23 ml/m 2 for MR; r00.76; p>0.05); RV ESV (46±21 ml/m 2 for CT vs. 43±18 ml/m 2 for MR; r00.70; p>0.05). Intra- and inter-observer variability were good, and the performance of CT was maintained for different EF subgroups. Conclusions Cardiac CT provides accurate and reproducible LVand RV volume parameters compared with MR, and can be considered as a reliable alternative for patients who are not suitable to undergo MR. Key Points • Cardiac-CT is able to provide Left and Right Ventricular function. • Cardiac-CT is accurate as MR for LV and RV volume assessment. • Cardiac-CT can provide accurate evaluation of coronary arteries and LV and RV function

Small aortic annulus: The hydrodynamic performances of 5 commercially available bileaflet mechanical valves

AbstractObjectiveHemodynamic performances of mechanical valve prostheses in patients with aortic valve stenosis and a small aortic annulus are crucial. We analyzed the in vitro hydrodynamics of 5 currently available bileaflet mechanical prostheses that fitted a 21-mm-diameter valve holder of a Sheffield pulse duplicator.MethodsThree samples of 5 high-performance production-quality prostheses, including the sewing ring cuffs, were tested in the aortic chamber of a Sheffield pulse duplicator. Sizes of the prostheses fitting the 21-mm valve holder were as follows: 18-mm ATS, 19-mm SJM Regent, 19-mm Sorin Bicarbon Slimline, 19-mm On-X, and 21-mm Carbomedics Top Hat. The tests were carried out at a fixed pulse rate (70 beats/min) and at increasing cardiac outputs of 2, 4, 5, and 7 L/min. Each valve was tested 10 times for each different cardiac output. This resulted in a total of 40 tests for each valve and 120 tests for each valve model. The aortic pressure was set at 120/80 mm Hg (mean pressure, 100 mm Hg) throughout the experiment for all cardiac outputs. Forward flow pressure decrease, total regurgitant volume, closing and leakage volumes, effective orifice area, and stroke work loss were recorded while the valve operated under each cardiac output.ResultsThe SJM Regent valve and the Sorin Bicarbon Slimline prosthesis showed the lowest mean and peak gradients at increasing cardiac outputs. The closure volume was higher for the SJM Regent and Sorin Bicarbon Slimline prostheses, unlike with the ATS prosthesis at 7 L/min of cardiac output. The ATS and SJM Regent prostheses showed the largest regurgitant volume, whereas the Sorin Bicarbon Slimline prosthesis showed the lowest regurgitant volume. The calculated effective orifice area and stroke work loss were significantly better with the SJM Regent and Sorin Bicarbon Slimline prostheses.ConclusionAssuming that the 21-mm valve holder in which all the tested prostheses were accommodated is comparable with a defined aortic valve size, this hydrodynamic evaluation model allowed us to compare the efficiency of currently available valve prostheses, and among these, the SJM Regent and the Sorin Bicarbon Slimline exhibited the best performances

A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic

The COVID-19 pandemic has heightened the existing concern about the uncertainty surrounding patient arrival and the overutilization of resources in emergency departments (EDs). The prediction of variations in patient arrivals is vital for managing limited healthcare resources and facilitating data-driven resource planning. The objective of this study was to forecast ED patient arrivals during a pandemic over different time horizons. A secondary objective was to compare the performance of different forecasting models in predicting ED patient arrivals. We included all ED patient encounters at an urban teaching hospital between January 2019 and December 2020. We divided the data into training and testing datasets and applied univariate and multivariable forecasting models to predict daily ED visits. The influence of COVID-19 lockdown and climatic factors were included in the multivariable models. The model evaluation consisted of the root mean square error (RMSE) and mean absolute error (MAE) over different forecasting horizons. Our exploratory analysis illustrated that monthly and weekly patterns impact daily demand for care. The Holt–Winters approach outperformed all other univariate and multivariable forecasting models for short-term predictions, while the Long Short-Term Memory approach performed best in extended predictions. The developed forecasting models are able to accurately predict ED patient arrivals and peaks during a surge when tested on two years of data from a high-volume urban ED. These short-and long-term prediction models can potentially enhance ED and hospital resource planning

Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG

Remote Photoplethysmography (rPPG) is a technology that utilizes the light absorption properties of hemoglobin, captured via camera, to analyze and measure blood volume pulse (BVP). By analyzing the measured BVP, various physiological signals such as heart rate, stress levels, and blood pressure can be derived, enabling applications such as the early prediction of cardiovascular diseases. rPPG is a rapidly evolving field as it allows the measurement of vital signals using camera-equipped devices without the need for additional devices such as blood pressure monitors or pulse oximeters, and without the assistance of medical experts. Despite extensive efforts and advances in this field, serious challenges remain, including issues related to skin color, camera characteristics, ambient lighting, and other sources of noise, which degrade performance accuracy. We argue that fair and evaluable benchmarking is urgently required to overcome these challenges and make any meaningful progress from both academic and commercial perspectives. In most existing work, models are trained, tested, and validated only on limited datasets. Worse still, some studies lack available code or reproducibility, making it difficult to fairly evaluate and compare performance. Therefore, the purpose of this study is to provide a benchmarking framework to evaluate various rPPG techniques across a wide range of datasets for fair evaluation and comparison, including both conventional non-deep neural network (non-DNN) and deep neural network (DNN) methods. GitHub URL: https://github.com/remotebiosensing/rppg.Comment: 19 pages, 10 figure

Teacher Self-evaluation Models as Authentic Portfolio to Monitor Language Teachers' Performance

Teachers may not feel satisfied with the feedback they have got from their superiors' evaluation. This paper aims at inspiring teachers with ideas of self-learning to improve their teaching performance for professional development. The writer shares his own experience as a principal and a head of the English department in exploring self-evaluation models to monitor language teachers' performance in the classroom