Modelling of urological dysfunctions with neurological etiology by means of their centres involved

A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.

System-on-chip design of the cortical-diencephalic centre of the lower urinary tract

This article presents the design of a field programmable gate array (FPGA)-based prototype of a system on chip (SoC) capable of behaving as one of the nerve centres comprising the neuroregulatory system in humans: the cortical-diencephalic nerve centre. The neuroregulatory system is a complex nerve system consisting of a heterogeneous group of nerve centres. These centres are distributed throughout the length of the spinal cord, are autonomous, communicate via interneurons, and govern and regulate the behaviour of multiple organs and systems in the human body. As a result of years of study of the functioning and composition of the neuroregulatory system of the lower urinary tract (LUT), the centres that regulate this system have been isolated. The objective of this study is to understand the individual functioning of each centre in order to create a general model of the neuroregulatory system that is capable of operating at the level of the actual nerve centre. This model represents an advancement of the current black box models that do not allow for isolated or independent treatment of system dysfunction. In this study, we re-visit our research into the viability of the hardware design of one of these centres-the cortical-diencephalic centre. We describe this hardware because functioning of the centre is completely configurable and programmable, which validates the design for other centres that comprise the neuroregulatory system. In this document, we succinctly present the formal model of the centre, propose a hardware design and an FPGA-based prototype, construct a testing and simulation environment to evaluate it and, lastly, analyse and contrast the results using data obtained from real patients, verifying that the functional behaviour fits that observed in humans.

Hardware design of the cortical-diencephalic centre of the lower urinary tract neuroregulator system

The neuroregulator system in humans controls organ and system functioning. This system comprises a set of neural centres that are distributed along the spinal cord and act independently together with their nerve interconnections. The centres involved in this task were isolated in previous studies through investigations of the functioning and composition of the neuroregulator system of the lower urinary tract to elucidate their individual performances and enable the creation of a general neuroregulator system model capable of operating at the neuronal level. Although the long-term goal of our research is the development of a system on chip (SoC) capable of behaving as a fully programmable neuroregulator system, this work is another step in which we test the viability of the hardware design of one of these neuroregulator centres (specifically the cortical-diencephalic centre) to achieve a first prototype and architectural proposal. To this end, the behaviour of this centre has been isolated, a hardware design implemented on FPGA has been proposed to create a prototype, a simulation environment has been built for the evaluation, and finally, the results have been analysed. This system verified that the functional behaviour corresponded to the expected behaviour in humans and that the operational requirements for the implementation were technically and architecturally viable.

Validation of artificial neural network models for predicting biochemical markers associated with male infertility

Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres.

ABBREVIATIONS

AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous epididymal sperm spiration; RBFN: radical basis function network; SRNN: simple recurrent neural network; SVM: support vector machines; TSE: testicular sperm extraction; WHO: World Health Organization.

Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

Mathematical modelling of the lower urinary tract

The lower urinary tract is one of the most complex biological systems of the human body as it involved hydrodynamic properties of urine and muscle. Moreover, its complexity is increased to be managed by voluntary and involuntary neural systems. In this paper, a mathematical model of the lower urinary tract it is proposed as a preliminary study to better understand its functioning. Furthermore, another goal of that mathematical model proposal is to provide a basis for developing artificial control systems. Lower urinary tract is comprised of two interacting systems: the mechanical system and the neural regulator. The latter has the function of controlling the mechanical system to perform the voiding process. The results of the tests reproduce experimental data with high degree of accuracy. Also, these results indicate that simulations not only with healthy patients but also of patients with dysfunctions with neurological etiology present urodynamic curves very similar to those obtained in clinical studies.