Application of Artificial Neural Networks to Identify Alzheimer’s Disease Using Cerebral Perfusion SPECT Data

Świetlik, Dariusz; Białowas, J

doi:10.3390/ijerph16071303

Cited by 38 publications

(22 citation statements)

References 26 publications

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“…There is sufficient scientific evidence proving the use of ANN in the health status, but few have been applied specifically to the elderly. 19,20 ANN can be an analysis model for investigating healthy aging, using simple and inexpensive questions that assess the elderly's perception about their life and health.…”

Section: Resultsmentioning

confidence: 99%

An artificial neural network to classify healthy aging in elderly Brazilians

Araujo¹,

Rocha²,

Alves³

et al. 2020

BBIJ

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Aging in Brazil, especially in the Amazon, is a complex and irregular process. Something is happening here that cannot be explained simply due to social inequalities. The objective of this study was to present the development of an artificial neural network and the stages of training, validation and testing for the classification of healthy aging among elderly Brazilians. We constructed a protocol for rapid diagnosis and health screening for the elderly. The form was developed offline in Microsoft Excel. Macros (routines capable of performing pre-programmed tasks) were created using Microsoft's Visual Basic for Applications (VBA) language. In the analysis of the confusion matrix, good accuracy were obtained in all stages, training (61.5%), validation (60.0%) and test (80.0%), which indicates that the network learned through the inputs and outputs initially defined and during the sample divisions performed for testing and validation. In the test stage, a ROC curve was obtained with better true positive rates and lower false positive rates, being close to the Y axis (left side), thus indicating better results. We conducted a pilot study with thirty-six community active elderlies from a city in Eastern Amazonia, Brazil. This study was divided into four parts: data collection, data pre-processing, training of an artificial neural network and evaluation methods.

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Section: Resultsmentioning

confidence: 99%

An artificial neural network to classify healthy aging in elderly Brazilians

Araujo¹,

Rocha²,

Alves³

et al. 2020

BBIJ

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“…These models were inspired by an exponential increase in the strength of synaptic connections, which preceded the loss of cholinergic connections. Diagnostic decision support of AD and analysis of SPECT images are the medical issues solved by means of artificial neural networks [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

Computer Model of Synapse Loss During an Alzheimer’s Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1—The Way to Chaos and Information Transfer

Świetlik

Białowas

Moryś

et al. 2019

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The aim of the study was to compare the computer model of synaptic breakdown in an Alzheimer’s disease-like pathology in the dentate gyrus (DG), CA3 and CA1 regions of the hippocampus with a control model using neuronal parameters and methods describing the complexity of the system, such as the correlative dimension, Shannon entropy and positive maximal Lyapunov exponent. The model of synaptic breakdown (from 13% to 50%) in the hippocampus modeling the dynamics of an Alzheimer’s disease-like pathology was simulated. Modeling consisted in turning off one after the other EC2 connections and connections from the dentate gyrus on the CA3 pyramidal neurons. The pathological model of synaptic disintegration was compared to a control. The larger synaptic breakdown was associated with a statistically significant decrease in the number of spikes (R = −0.79, P < 0.001), spikes per burst (R = −0.76, P < 0.001) and burst duration (R = −0.83, P < 0.001) and an increase in the inter-burst interval (R = 0.85, P < 0.001) in DG-CA3-CA1. The positive maximal Lyapunov exponent in the control model was negative, but in the pathological model had a positive value of DG-CA3-CA1. A statistically significant decrease of Shannon entropy with the direction of information flow DG->CA3->CA1 (R = −0.79, P < 0.001) in the pathological model and a statistically significant increase with greater synaptic breakdown (R = 0.24, P < 0.05) of the CA3-CA1 region was obtained. The reduction of entropy transfer for DG->CA3 at the level of synaptic breakdown of 35% was 35%, compared with the control. Entropy transfer for CA3->CA1 at the level of synaptic breakdown of 35% increased to 95% relative to the control. The synaptic breakdown model in an Alzheimer’s disease-like pathology in DG-CA3-CA1 exhibits chaotic features as opposed to the control. Synaptic breakdown in which an increase of Shannon entropy is observed indicates an irreversible process of Alzheimer’s disease. The increase in synapse loss resulted in decreased information flow and entropy transfer in DG->CA3, and at the same time a strong increase in CA3->CA1.

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“…The joint HCPMMP (J-HCPMMP) basically outlined the cortical architecture, function, and connectivity features related to AD and different stages of MCI and achieved the highest accuracy with SVM classifier for each group of subjects. In [43], the authors applied a three layer (input, hidden, and output) ANN to illustrate its effectiveness in AD diagnosis. The diagnosis was based on SPECT brain images of cerebral blood flow of 132 subjects with 72 AD patients and 60 NC.…”

Section: ) Ml-based Approaches In Ad Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Application of Artificial Neural Networks to Identify Alzheimer’s Disease Using Cerebral Perfusion SPECT Data

Cited by 38 publications

References 26 publications

An artificial neural network to classify healthy aging in elderly Brazilians

An artificial neural network to classify healthy aging in elderly Brazilians

Computer Model of Synapse Loss During an Alzheimer’s Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1—The Way to Chaos and Information Transfer

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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