The use of 3D surface fitting for robust polyp detection and classification in CT colonography

In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the evaluation of the surface morphology that is employed for the detection of colonic polyps in computed tomography (CT) colonography. Initial polyp candidate voxels were detected using the surface normal intersection values. These candidate voxels were clustered using the normal direction, convexity test, region growing and Gaussian distribution. The local colonic surface was classified as polyp or fold using a feature normalized nearest neighborhood classifier. The main merit of this paper is the methodology applied to select the robust features derived from the colon surface that have a high discriminative power for polyp/fold classification. The devised polyp detection scheme entails a low computational overhead (typically takes 2.20 min per dataset) and shows 100% sensitivity for phantom polyps greater than 5 mm. It also shows 100% sensitivity for real polyps larger than 10 mm and 91.67% sensitivity for polyps between 5 to 10 mm with an average of 4.5 false positives per dataset. The experimental data indicates that the proposed CAD polyp detection scheme outperforms other techniques that identify the polyps using features that sample the colon surface curvature especially when applied to low-dose datasets.     

Effect on EEG responses of removing ocular artefacts by proportional EOG subtraction

Work undertaken to investigate the effect on EEG responses of ocular artefact removal by proportional electro-oculogram subtraction using the least-squares method is reported. The ocular artefact model and the least-squares approach to estimation of the model parameters and hence of the EEG waveform, including the response, are described. Results are presented to show that the response shapes become modified if ocular artefact removal is implemented recursively. This happens because the response is incorrectly treated as part of the random background EEG. The solution is to incorporate a model of the response within the ocular artefact removal algorithm. The results of tests on simulated data confirm these conclusions. The results of the incorporation of response models in removing ocular artefacts from CNV recordings are presented. Even with modelling recursive ocular artefact removal is found to modify the shape of the CNV, and so reliable results can only be obtained if the removal is implemented nonrecursively. Evidence is given that it is advisable to remove DC levels from the recorded data.     

Nonhomogeneous Deformation in the Anterior Leaflet of the Mitral Valve

In the mitral valve, regional variations in structure and material properties combine to affect the biomechanics of the entire valve. Previous biaxial testing has shown that mitral valve leaflet tissue is highly extensible, and exhibits nonlinear, anisotropic material properties. In this study, experimental measurements of mitral valve leaflet deformation under quasi-static pressure loading were performed on isolated porcine hearts. Biplane video images of markers placed on the anterior leaflet surface were used to reconstruct the 3D position of the markers at several pressure levels over the physiological range. A least-squares finite-element method was used to fit parametric models to the markers and to calculate the deformation over the surface. The results showed that the leaflet deformations were anisotropic, exhibiting a large nonhomogeneous radial stretch and a small circumferential stretch. This information can be used to better understand how the valve deforms under physiological loading, and to help design treatments for valve problems, such as mitral regurgitation.     

Removal of cardiac beat artifact in oesophageal pressure measurement by frequency analysis

Oesophageal pressure (Pes) measurements are important in medical research and useful in clinical diagnosis. Measurements, however, are contaminated heavily by cardiac artifacts. The spectrum and waveform of the Pes signal is obtained from the oesophageal balloon. Adaptive finite impulse response (AFIR) filter and modified adaptive noise cancellation (MANC) methods are adopted to filter out cardiac beat interference. These results are compared. In the frequency domain, frequency variations and spectral overlap between the Pes components and cardiac beat signal components impact on the performance of the filter. From our experimental results on power strength, the fourth or higher harmonics did not have any significant effect on the filter performance. However, the second harmonics of these signals had a significant effect on the filtering result. Thus, in the design of AFIR filters, attention is needed to minimise these effects. In frequency analysis, these harmonics or overlapping frequencies do not affect MANC. MANC was the better method for eliminating cardiac beat artifact in Pes measurement. The dynamic compliance (Cdyn) was also used to evaluate the performance of MANC and AFIR. The standard deviation of Cdyn was less than 0.15 using MANC, compared with standard deviations as high as 0.57 for AFIR. We conclude that MANC performs better than AFIR.     

基于聚类分析的径向基神经网络用于证候诊断的研究

目的:优化中医证候诊断模型,为中医证候诊断标准的研究提供可行性方法.方法:提出用于中医证候诊断的径向基(Radial Basis Function,RBF)神经网络,利用聚类分析确定RBF神经网络隐层的参数,运用最小二乘确定RBF神经网络输出层的参数.结果:通过模型检验,证候诊断模型判准率比BP网络模型判准率高;证候诊断模型训练速度比BP网络模型快.结论:基于聚类分析的RBF神经网络用于中医证候诊断的研究是可行的和有效的.     

Predicting healthcare expenditure by multimorbidity groups

Objectives

This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation of health budgets.

Guidelines for selecting among different types of bootstraps

ABSTRACT

Background: The bootstrap has become very popular in health economics. Its success lies in the ease of estimating sampling distribution, standard error and confidence intervals with few or no assumptions about the distribution of the underlying population.

Objective: The purpose of this paper is three-fold: (1) to provide an overview of four common bootstrap techniques for readers who have little or no statistical background; (2) to suggest a guideline for selecting the most applicable bootstrap technique for your data; and (3) to connect guidelines with a real world example, to illustrate how different bootstraps behave in one model, or in different models.

Results: The assumptions of homoscedasticity and normality are key to selecting the best bootstrapping technique. These assumptions should be tested before applying any bootstrapping technique. If homoscedasticity and normality hold, then parametric bootstrapping is consistent and efficient. Paired and wild bootstrapping are consistent under heteroscedasticity and non-normality assumptions.

Conclusion: Selecting the correct type of bootstrapping is crucial for arriving at efficient estimators. Our example illustrates that if we selected an inconsistent bootstrapping technique, results could be misleading. An insignificant effect of controller treatment on total health expenditures among asthma patients would have been found significant and negative by an improperly chosen bootstrapping technique, regardless of the type of model chosen.     

Research of fetal ECG extraction using wavelet analysis and adaptive filtering

Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.     

Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19

BackgroundThe evolution of patients hospitalized with coronavirus disease 2019 (COVID-19) is still hard to predict, even after several months of dealing with the pandemic.AimsTo develop and validate a score to predict outcomes in patients hospitalized with COVID-19.MethodsAll consecutive adults hospitalized for COVID-19 from February to April 2020 were included in a nationwide observational study. Primary composite outcome was transfer to an intensive care unit from an emergency department or conventional ward, or in-hospital death. A score that estimates the risk of experiencing the primary outcome was constructed from a derivation cohort using stacked LASSO (Least Absolute Shrinkage and Selection Operator), and was tested in a validation cohort.ResultsAmong 2873 patients analysed (57.9% men; 66.6 ± 17.0 years), the primary outcome occurred in 838 (29.2%) patients: 551 (19.2%) were transferred to an intensive care unit; and 287 (10.0%) died in-hospital without transfer to an intensive care unit. Using stacked LASSO, we identified 11 variables independently associated with the primary outcome in multivariable analysis in the derivation cohort (n = 2313), including demographics (sex), triage vitals (body temperature, dyspnoea, respiratory rate, fraction of inspired oxygen, blood oxygen saturation) and biological variables (pH, platelets, C-reactive protein, aspartate aminotransferase, estimated glomerular filtration rate). The Critical COVID-19 France (CCF) risk score was then developed, and displayed accurate calibration and discrimination in the derivation cohort, with C-statistics of 0.78 (95% confidence interval 0.75–0.80). The CCF risk score performed significantly better (i.e. higher C-statistics) than the usual critical care risk scores.ConclusionsThe CCF risk score was built using data collected routinely at hospital admission to predict outcomes in patients with COVID-19. This score holds promise to improve early triage of patients and allocation of healthcare resources.