Automatic computation of regions of interest by robust principal component analysis. Application to automatic dementia diagnosis

Event Surrogate from Clinical Pathway Completion to Daily Meal for Availability Extension Using Standard Electronic Medical Records: a Retrospective Cohort Study

This study aimed to improve generalizability of our previous study that analyzed clinical pathway (CP) completion. Although our previous study demonstrated that CP completion can reduce the length of hospital stay, it is possible for few medical organizations to extract the implementation of treatment registered on CP from typical electronic medical records. Therefore, we have defined a prospective event for event substitution, called meal completion (MC), in which patients can take their meal daily. Data were collected from April 2013 to March 2018 from the electronic medical records of the University of Miyazaki Hospital. We used propensity score matching to extract records from 8033 patients. Patients were further divided into the MC and non-MC groups; 2577 patients in each group were available for data analysis. The numbers of patients with CP completion were 646 (28.1%) in the MC group and 411 (18.2%) in the non-MC group. The P value of the chi-square test was <0.001. According to this result, there was the causation from MC to increase in CP completion. Additionally, it was possible to consider the inclusion relationship in all treatments (universal set), treatments registered on CP (subset of all treatments), and meals (subset of treatments registered on CP). In conclusion, MC can substitute for CP completion because the demonstration is appropriate for the Prentice criterion, which is often used for the evaluation of a surrogate endpoint.

Research on the Establishment and Application of the Environmental Health Indicator System of Atmospheric Pollution in China

To understand the health impact represented by exposure to current atmospheric pollution in China, an environmental health indicators (EHIs) system of atmospheric pollution was established. The EHIs were based on comprehensive consideration of environment, population, economy and diseases associated with atmospheric pollution. An EHIs evaluation system of atmospheric pollution, based on corresponding EHIs data collection and weighting coefficients determined using principal component analysis, was applied to major provinces and regions in China to evaluate the environmental health status. Results showed that the EHIs of atmospheric pollution in Central and East China were low, indicating a serious environmental health condition. Prevention and management of atmospheric pollution in these regions should be strengthened and protective measures taken to improve human health. Compared with other methods, the EHIs evaluation system was more intuitive, which facilitated users to identify the environmental health status and provided support for health management and pollution prevention.

Parkinson's Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks

Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson's disease, whose ultimate goal is the detection by automatic recognizing of patterns that characterize the disease. In recent times Convolutional Neural Networks (CNN) have proved to be amazingly useful for that task. The drawback, however, is that 3D brain images contain a huge amount of information that leads to complex CNN architectures. When these architectures become too complex, classification performances often degrades because the limitations of the training algorithm and overfitting. Thus, this paper proposes the use of isosurfaces as a way to reduce such amount of data while keeping the most relevant information. These isosurfaces are then used to implement a classification system which uses two of the most well-known CNN architectures, LeNet and AlexNet, to classify DaTScan images with an average accuracy of 95.1% and AUC = 97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computational burden.

Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.