Artificial neural network simulation of arm gait of Huntington disease patient

Ajibola, O.O.E.; V.O.S., Olunloyo

doi:10.1504/ijbbr.2011.040031

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…Figure 1 is the outcome of our effort in this endeavour and it is in agreement with previous works in literature. The outcome of our research effort is in agreement with the existing literature [1,5,9,13]. Figure 3 is a comparative analysis of the electromechanical model of gait phenomenon of [12] and the GA simulation model.…”

Section: Resultssupporting

confidence: 86%

“…Dopamine and glutamate transmission and interactions are affected, contributing to striatal and cortical vulnerability featuring such presentations as chorea, [1,7,8]. Most agents investigated for HD chorea target the neuro transmitters and receptors, [9,10,11]. The current management of HD is focused on symptom reduction, because there is no treatment capable of halting the progressive global deterioration and eventual death occurring within 10-20 years of disease onset.…”

Section: Introductionmentioning

confidence: 99%

“…Our earlier attempts at capturing the gait phenomenon have produced positive results by way of mathematical modelling: electromechanical, artificial neural network (ANN) simulation, fuzzy set and fuzzy logic modelling techniques. One outstanding result described by [13] is the biometry of gait models based on artificial neural network simulation and the electromechanical techniques the graphical representation of which is presented in Figure 1. Figure 1 relates in some details the dynamic behaviour of the arm of HD patients in an electromechanical equation described in Cartesian coordinates with the ANN simulation of the same movement.…”

Section: Introductionmentioning

confidence: 99%

“…The two curves ably captured the phenomenon between time T = 0 and time T = 28.5 representing a steady build-up of excitatory post synaptic potentiation (EPSP) that culminates in the potentiation threshold at T = 28 milliseconds which explains the fact that they both approach threshold at approximately the same rate. This is the point in time when the inhibitory post synaptic potentiation (IPSP) completely failed to match the EPSP thereby resulting in a jerk [9]. Since the etiology of HD has been traced to a mutation in chromosome 4p15, and the pathogenesis of the disease is well known: it is practicable to deploy the genetic algorithm in the modelling of gait syndrome in HD for its probabilistic properties since the precipitation of action potential is not a sufficient but only a necessary condition for the production of chorea in an HD patient.…”

Section: Introductionmentioning

confidence: 99%

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MODELLING GAIT SYNDROME IN HUNTINGTONâ€™S DISEASE: THE GENETIC ALGORITHM APPROACH

Ajibola

Ogunwolu

2017

Nig. J. Tech.

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Huntingtonâ€™s disease (HD) which usually affects the patients at middle age results from malfunctioning of the basal ganglia. It is characterized by cognitive impairment, involuntary movements, neuropsychiatric and psychological disturbances. Early motor signs of Huntingtonâ€™s disease typically include the gradual onset of clumsiness, balance difficulties, and brief, random, fidgeting movements. A popular approach to solving symptoms arising from HD has been through the administration of drugs. But drugs debase human activities, thus the application of electroconvulsive therapy. This work proposes a genetic algorithmic (GA) simulation of chorea in HD patient as a pedestal for the design of a therapeutic device aimed at managing the phenomenon. Earlier efforts have led us to the formulation of such models in recent past. We also compared the GA model with our foremost effort: the electromechanical model, and we observe that the GA model adequately capture the physiological presentation of gait phenomenon in Huntingtonâ€™s disease.Â http://dx.doi.org/10.4314/njt.v36i3.37

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Section: Resultssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MODELLING GAIT SYNDROME IN HUNTINGTONâ€™S DISEASE: THE GENETIC ALGORITHM APPROACH

Ajibola

Ogunwolu

2017

Nig. J. Tech.

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“…For efficiency, there must be sufficient number of images for the training of the algorithm in two sets: the training and the test data. Traditionally, the ratio of training to test thermograms is 7:3 of the population of selected thermograms with the number of normal breast thermograms overwhelmingly outweighing that of cancerous breasts (Ajibola et al, 2011;Ibiwoye et al, 2012). The anomaly or outlier detection algorithm is trained in the same manner an artificial neural network is trained using the Gaussian distribution function to identify thermograms that do not conform to an expected dataset.…”

Section: Training Location Detectors and Filtering Warmmentioning

confidence: 99%

Exterior Means for Premature Recognition of Breast Cancer

Ajibola¹,

Babafemi²,

Adedeji³

et al. 2018

Journal of Applied Sciences and Environmental Management

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This research is aimed at the early detection of breast cancer. Breast cancer is a form of cancer that is found in the breast tissue. Though mostly common in women, research has shown that it can also be diagnosed in men. The increasing trend in the mortality rate attributable to breast cancer can be traced to inefficient methods of detecting the cancer in its early stage. Current methods involve invasion, exposure of the patient to radiation, and/or compressing the breast. These may cause pain or even invest patient with other forms of ailments. In this work, infrared (IR) thermography has been deployed to detect breast cancer in its early stage. IR thermography uses IR radiation to measure heat patterns of human skin. It is passive in nature and it neither emit harmful radiation nor subject the patient to further risks. The thermograms captured by infrared camera are analysed by a software in stages, viz: extraction of region of interest, detection and masking of warm region among others. The resulting image in grayscale is sampled by comparing the white pixels (warm region) to the dark pixels (cool region). The software subsequently compares the outcome with a predefined threshold to predict the chances of occurrence of cancer and displays result for further diagnosis by medical expert. Our proposed modality is cost effective, safe and user friendly.

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Artificial intelligence in diagnosis and management of Huntington’s disease

Parekh,

Bhagat,

Raj

et al. 2023

Beni-Suef Univ J Basic Appl Sci

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Background Huntington’s disease is one of the rare neurodegenerative diseases caused because of genetic mutation of the Huntingtin gene. The major hallmarks of the condition include motor impairment, cognitive decline, and psychiatric symptoms. With no cure and only symptomatic treatments available, early detection and personalized therapy are warranted for managing the disease effectively. Artificial Intelligence has emerged as a transformational tool in healthcare, revolutionizing many parts of medical practice and research, thus holding the potential in detecting, monitoring, and managing Huntington’s disease. Main body of abstract Artificial Intelligence’s role in Huntington’s disease includes a variety of applications like medical image analysis and predictive analytics. AI-driven algorithms are utilized to analyze brain imaging data in medical image analysis. Deep learning and convolutional neural networks (CNNs) aid in the detection of subtle brain changes and the identification of illness biomarkers, allowing for the early diagnosis of the disease. Additionally, the predictive analytics capabilities of AI are used to analyze disease development and forecast clinical outcomes. AI models can identify illness patterns, estimate the rate of functional decline, and assist doctors in making educated decisions about treatment methods and care planning by analyzing patient data. Conclusions With clinical practice and research integrated with Artificial Intelligence technologies, we can significantly improve the quality of life of individuals affected with Huntington’s disease. This integration holds the potential to develop effective personalized interventions. Nevertheless, collaborative efforts among doctors, researchers, and technology sound developers would be key to the successful implementation of AI in HD. Graphical Abstract

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Artificial neural network simulation of arm gait of Huntington disease patient

Cited by 4 publications

References 8 publications

MODELLING GAIT SYNDROME IN HUNTINGTONâ€™S DISEASE: THE GENETIC ALGORITHM APPROACH

MODELLING GAIT SYNDROME IN HUNTINGTONâ€™S DISEASE: THE GENETIC ALGORITHM APPROACH

Exterior Means for Premature Recognition of Breast Cancer

Artificial intelligence in diagnosis and management of Huntington’s disease

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