Prediction of Alzheimer's Disease from Magnetic Resonance Imaging using a Convolutional Neural Network

OBJECTIVES: The primary goal of this study is to examine if a convolutional neural network (CNN) can be applied as a diagnostic tool for predicting Alzheimer's Disease (AD) from magnetic resonance imaging (MRI) using the MIRIAD-dataset (Minimal Interval Resonance Imaging in Alzheimer's Disease) from one single central slice of the brain. METHODS: The MIRIAD dataset contains patients' health records represented by a set of MRI scans of the brain and further diagnostic data. Hyperparameters and configurations of CNNs were optimized to determine the best-performing model. The CNN was implemented in Python with the deep learning library ‘Keras’ using Linux/Ubuntu as the operating system. RESULTS: This study obtained the following best performance metrics for predicting Alzheimer's Disease from MRI with Matthew's Correlation Coefficient (MCC) of 0.77; accuracy of 0.89; F1-score of 0.89; AUC of 0.92. The computational time for the training of a CNN takes less than 30 sec. s with a GPU (graphics processing unit). The prediction takes less than 1 sec. on a standard PC. CONCLUSIONS: The study suggests that an axial MRI scan can be used to diagnose if a patient has Alzheimer's Disease with an AUC score of 0.92

Inorganic nanoclusters in organic glasses — Novel materials for electro-optical applications

Polymer glasses which contain regularly arranged ultrasmall inorganic crystallites or clusters of CdS, CoS, NiS, ZnS have been prepared from functionalized diblock copolymers. Size and surface structure dependent variation of the ionization or redox potential respectively the band gap energy of the semiconductor particles in the polymeric glasses can be exploited to control photochemical processes and optical properties. The combination of anorganic and organic compounds provides a simple route to highly ordered, stable and processable materials with a wide range of properties. Regular arrangement of the clusters in a defined supermolecular lattice might be used for tayloring electromagnetic interactions and might provide new materials for infrared and microwave applications

A Machine Learning Solution to Predict Elective Orthopedic Surgery Case Duration.

We used surgery durations, patient demographic and personnel data taken from the East Kent Hospitals University NHS Foundation Trust (EKHUFT) over a period of 10 years (2010-2019) for a total of 25,352 patients that underwent 15 highest volume elective orthopedic surgeries, to predict future surgery durations for the subset of elective surgeries under consideration. As part of this study, we compared two different ensemble machine learning methods random forest regression (RF) and XGBoost (eXtreme Gradient Boosting) regression. The two models were approximately 5% superior to the existing model used by the hospital scheduling system

Prediction of Waiting Times in A&E

Predicting waiting times in A&E is a critical tool for controlling the flow of patients in the department. The most used method (rolling average) does not account for the complex context of the A&E. Using retrospective data of patients visiting an A&E service from 2017 to 2019 (pre-pandemic). An AI-enabled method is used to predict waiting times in this study. A random forest and XGBoost regression methods were trained and tested to predict the time to discharge before the patient arrived at the hospital. When applying the final models to the 68,321 observations and using the complete set of features, the random forest algorithm’s performance measurements are RMSE=85.31 and MAE=66.71. The XGBoost model obtained a performance of RMSE=82.66 and MAE=64.31. The approach might be a more dynamic method to predict waiting times

Novel form-flexible handling and joining tool for automated preforming

The production rates of carbon fiber reinforced plastic (CFRP) parts are rising constantly which in turn drives research to bring a higher level of automation to the manufacturing processes of CFRP. Resin transfer molding (RTM), which is seen as a production method for high volumes, has been accelerated to a high degree. However, complex net-shape preforms are necessary for this process, which are widely manually manufactured. To face these challenges a new concept for the manufacturing of carbon fiber preforms with a form-flexible gripping, draping and joining end-effector is presented and discussed. Furthermore, this paper investigates the application of this concept, describes the initial build-up of a demonstrator, focusing on material selection and heating technology, and discusses test results with the prototype. This prototype already validates the feasibility of the proposed concept on the basis of a generic preform geometry. After a summary, this paper discusses future in-depth research concerning the concept and its application in more complex geometries. © 2015 by De Gruyter 2015

Form-flexible handling and joining technology (formhand) for the forming and assembly of limp materials

The assembly of limp, elastic or differently shaped objects poses a huge challenge which needs to be met by machine tools and the corresponding processes of handling, forming and joining. These processes are often carried out manually. This technological gap triggered the present work at the Technische Universität Braunschweig. A novel form-flexible handling tool (FormHand) is presented which focuses on the automation of these production steps taking into consideration the material behavior. The combination of the flexibility of both industrial robot and the FormHand end-effector allows for new processes appropriate for these materials. This article investigates the used materials of the granular filler and the cushion textile, the working states of FormHand and the use of online sensors for an automated process application

Reduced Serum Levels of Bone Formation Marker P1NP in Psoriasis

Psoriasis is a chronic inflammatory disease of the skin and joints. More recent data emphasize an association with dysregulated glucose and fatty acid metabolism, obesity, elevated blood pressure and cardiac disease, summarized as metabolic syndrome. TNF-a and IL-17, central players in the pathogenesis of psoriasis, are known to impair bone formation. Therefore, the relation between psoriasis and bone metabolism parameters was investigated. Two serum markers of either bone formation—N-terminal propeptide of type I procollagen (P1NP) or bone resorption—C-terminal telopeptide of type I collagen (CTX-I)—were analyzed in a cohort of patients with psoriasis vulgaris. In patients with psoriasis, P1NP serum levels were reduced compared to gender-, age-, and body mass index-matched healthy controls. CTX-I levels were indistinguishable between patients with psoriasis and controls. Consistently, induction of psoriasis-like skin inflammation in mice decreases bone volume and activity of osteoblasts. Moreover, efficient anti-psoriatic treatment improved psoriasis severity, but did not reverse decreased P1NP level suggesting that independent of efficient skin treatment psoriasis did affect bone metabolism and might favor the development of osteoporosis. Taken together, evidence is provided that bone metabolism might be affected by psoriatic inflammation, which may have consequences for future patient counseling and disease monitoring