Modelling of urological dysfunctions with neurological etiology by means of their centres involved (original) (raw)
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Artificial Neural Networks for Diagnoses of Dysfunctions in Urology
Proceedings of the First International Conference on Health Informatics, 2008
In this article we evaluate the work out of artificial neural networks as tools for helping and support in the medical diagnosis. In particular we compare the usability of one supervised and two unsupervised neural network architectures for medical diagnoses of lower urinary tract dysfunctions. The purpose is to develop a system that aid urologists in obtaining diagnoses, which will yield improved diagnostic accuracy and lower medical treatment costs. The clinical study has been carried out using the medical registers of patients with dysfunctions in the lower urinary tract. The current system is able to distinguish and classify dysfunctions as areflexia, hyperreflexia, obstruction of the lower urinary tract and patients free from dysfunction.
Development of an artificial neural network for helping to diagnose diseases in urology
2006 1st Bio-Inspired Models of Network, Information and Computing Systems, 2006
In this article we propose the development of a tool for helping in the medical diagnosis using neural networks, in particular the multilayer perceptron. This new tool is meant to help the urologists in obtaining an automatic diagnosis for complex multi-variable systems, and to avoid painful and costly medical treatments. The clinical study has been carried out using the medical registers of patients with dysfunctions in the lower urinary tract. The system is able to distinguish and classify dysfunctions as arreflexive, hyper-reflexive, and effort incontinence. Moreover, it is able to predict whether there is presence of dysfunction or not. The results of the experiments display a high percentage of certainty of about 85 %.
Modeling of Neuropathic Bladder Lesions Diagnosis Using Neural Network Algorithm.
The urinary bladder is probably the only visceral smooth-muscle organ that is under complete voluntary control from the cerebral cortex. Normal bladder function requires interaction of sensory and motor components of both the somatic and autonomic nervous system. Recent advances in the understanding of neural pathways and neurotransmitters have shown that most levels of the nervous system are involved in the regulation of voiding function. Therefore many neuralgic diseases causes changes in the bladder function [1]. In this paper, Number of patients selected from Ibn-Alkiff hospital (for treatment and rehabilitation of Spinal cord injuries), in Baghdad, who were referred to the urology department for complains of some urinary symptoms, and examined by cystometry in the urology out patient and/or inpatient department. These cases were selected randomly who already consult these departments and were followed up and managed by the expert urosurgeons. They were adults complaining of general neuropathic bladder disorder symptoms like frequency, urgency, dysuria, urinary incontinence and were diagnosed as having neuropathic bladder disease, whether: 1. Upper motor neuropathic bladder lesions. 2. Lower motor neuropathic bladder lesions. And finally they were examined by cystometry. The collections of data from patients were about: 1. Accommodation (compliance). 2. Bladder capacity. 3. Contractility. 4. Sensation. 5. Voluntary control. These data with the final definition diagnosis about the neuropathic bladder lesion were processed to 3- layers Neural Network algorithm that was constructed in a matlab computer package. Consequently after all data processing, the neural network model was tested by its capability of processing an already diagnosed neuropathic bladder case and its accuracy in explaining the real neurological bladder behavior of that selected patient.
Comparison of different computer models of the neural control system of the lower urinary tract
Neurourology and Urodynamics, 2000
This paper presents a series of five models that were formulated for describing the neural control of the lower urinary tract in humans. A parsimonious formulation of the effect of the sympathetic system, the pre-optic area, and urethral afferents on the simulated behavior are included. In spite of the relative simplicity of the five models studied, behavior that resembles normal lower urinary tract behavior as seen during an urodynamic investigation could be simulated. The models were tested by studying their response to disturbances of the afferent signal from the bladder. It was found that the inhibiting reflex that results from including the sympathetic system or the pre-optic area (PrOA) only counteracts the disturbance in the storage phase. Once micturition has started, these inhibiting reflexes are suppressed. A detrusor contraction that does not result in complete micturition similar to an unstable detrusor contraction could be simulated in a model including urethral afferents. Owing to the number of uncertainties in these models, so far no unambiguous explanation of normal and pathological lower urinary tract behavior can be given. However, these models can be used as an additional tool in studies of the mechanisms of the involved neural control.
Application of artificial neural networks in the diagnosis of urological dysfunctions
Expert Systems with Applications, 2009
The present research is aimed to develop an ANN diagnostic model for the coronary atherosclerosis and ischemia for patients after coronary angiography on the basis of genetic, clinical laboratory and instrumental examination data. The analysis of the correlation between the signs allowed us to choose the factors most closely connected with the diagnosis. Hierarchical clustering and correlation analysis were adapted to allocate typical fields of diagnostic factors. Various types of ANN topologies (MLP, SVM, PCA, and hybrid network) were analyzed; we have found that the models based on ANN with principal components analysis, and double-layer perceptron ANN optimized with genetic algorithms achieve the best diagnostic efficacy.
Expert Systems With Applications, 2009
Uroflowmetry is a measuring method, which provides numerical and graphical information about patient's lower urinary tract dynamics by measuring and plotting the rate of change in urine volume. The main purpose of this study is to analyze the uroflowmetric data and to assist physicians for their diagnosis. An expert pre-diagnosis system is implemented for automatically evaluating possible symptoms from the uroflow signals. The system uses artificial neural networks (ANN) and produces a pre-diagnostic result. The outputs of ANN are classified into three groups, which are, ''healthy", ''possible pathologic" and ''pathologic". The ANN is trained using back-propagation method and the inputs of the ANN are the extracted features, which are selected according to the suggestions of urology specialists. The proposed system is trained and validated using a dataset of patients, who have already diagnosed by the specialists.
A Computer model of the neural control of the lower urinary tract
Neurourology and Urodynamics, 1998
Better understanding of the underlying working mechanism of the neural control of the lower urinary tract will facilitate the treatment of dysfunction with a neurogenic cause. We developed a computer model to study the effect of a neural control system on lower urinary tract behavior. To model the mechanical properties and neural control, assumptions had to be made. These assumptions were based, as much as possible, on knowledge and hypotheses taken from the literature. With valid assumptions, it should be possible to simulate normal as well as pathological behavior. To test the computer model, first, normal behavior of the lower urinary tract was simulated, and secondly, the known features of bladder outlet obstruction were simulated after the properties of the urethra were changed. The simulation results are comparable with measured data, so the assumptions on which the model is based could be valid. If the assumptions are valid, the feedback loops used in the model are also important feedback loops in vivo, and the model can be used to gain insight into the underlying mechanism of neural control.
Pathological Analysis of Stress Urinary Incontinence in Females using Artificial Neural Networks
arXiv (Cornell University), 2018
Objectives: To mathematically investigate urethral pressure and influencing parameters of stress urinary incontinence (SUI) in women, with focus on the clinical aspects of the mathematical modeling. Method: Several patients' data are extracted from UPP and urodynamic documents and their relation and affinities are modeled using an artificial neural network (ANN) model. The studied parameter is urethral pressure as a function of two variables: the age of the patient and the position in which the pressure was measured across the urethra (normalized length). Results: The ANN-generated surface, showing the relation between the chosen parameters and the urethral pressure in the studied patients, is more efficient than the surface generated by conventional mathematical methods for clinical analysis, with multi-sample analysis being obtained. For example, in elderly people, there are many low-pressure zones throughout the urethra length, indicating that there is more incontinence in old age. Conclusion: The predictions of urethral pressure made by the trained neural network model in relation to the studied effective parameters can be used to build a medical assistance system in order to help clinicians diagnose urinary incontinence problems more efficiently.
Diagnosis of Bladder Outlet Obstruction By Quantitative Features Using Neural Networks
Ain Shams 2nd International …, 2004
This paper presents a neural network system to classifi patients of lower urinary tract symptoms (LUTS) and obtain their degree of bladder outlet obstruction (BOO) according to linear passive urethral resistance relation (PURR) nomogram or schafer grade (0 or I ) for nonobstructed flow, 2 for equivocal and (3,4,5 or 6) for obstructed patient. LUTS patients received routine investigation, consisting of transrectal ultrasonography of the prostate, serum PSA measurement, assessment of symptoms and quality of lye by the International Prostate Symptom Score (IPSS)), urinary flowmetry with determination of maximum flow rate, voided volume and post-void residual urine and full pressure flow studies (PFS) which are the best available method to distinguish BOO, But PFS are too invasive and time-consuming and expensive to be routinely utilized. Thus an Artificial Neural Network (ANN) was constructed to estimate the degree of obstruction (schafer grade). The input to the ANN consisted of four readings (average flow rate A-F-R, maximum flow rate M-F-R, prostate size as measured by transrectal ultrasound TRUS and residual urine Res-Urin) which are most sign$cant and less invasive. The performance of the ANN classij?er was compared with that of a minimum distance and a voting k nearest neighbor classifiers. The ANN revealed better results than both two classifers.
2002
The robustness that shows the biological regulation of the human lower urinary tract provides a suggestive paradigm for the artificial control. The biological regulator consists of a heterogeneous group of nervous centres that act cooperatively, in a distributed way. That regulation conforms a behaviour of several types (autonomous work or conscious one) and it reduces the consequences in situations of bad operation. Related to that system, we propose a model of the paradigm of heterogeneous and distributed control that can be found in biological systems. The objective is to artificially reproduce the benefits of robustness in order to use it in the control of natural systems and artificial devices. The distributed aspects have been obtained using multiple intelligent agents, each one of which represents one of the biological centres. The interaction pattern among agents provides a heuristic based on the OAM neural network (Orthogonal Associative Memory). The knowledge has been added to the system by training, using correct patterns of behaviour of the urinary tract and wrong behaviour patterns due to the inoperability in up to two of the agents (representing deficiencies in up to two nervous centres at the same time). The experiments show that the model is robust and it satisfies the expectations of providing a model of the regulator system that allows to break into fragments the problem, in simple modules with own entity each.