Using neural networks for differential diagnosis of alzheimer disease and vascular dementia
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Using neural networks for differential diagnosis of Alzheimer Disease and Vascular Dementia. Author: Elizabeth Gaarcia-Perez, Arturo Violante, Francisco Cervantes-Perez Expert Systems with Applications. Introduction.

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Using neural networks for differential diagnosis of Alzheimer Disease and Vascular Dementia

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Using neural networks for differential diagnosis of alzheimer disease and vascular dementia

Using neural networks for differential diagnosis of Alzheimer Disease and Vascular Dementia

Author: Elizabeth Gaarcia-Perez, Arturo Violante, Francisco Cervantes-Perez

Expert Systems with Applications


Introduction

Introduction

  • Several studies have shown that in people 65 years old or older, the presence of Alzheimcr Disease (AD) has increased from 1.3 to 6.2% (Ueda & Kawano, 1992; Gorelick & Roman, 1993; Joachin et al., 1988)

  • the Mexican Society for Alzheimer has reported that 6% of the people over 65 years of age have been diagnosed with Alzheimer (Cummings & Benson, 1992; Friedland, 1993)


Introduction1

Introduction

  • Within the analysis of dementia, the diagnosis of AD and VD is one of the main concerns, they represent almost 90% of the illnesses presented by patients with dementia (O'Brien, 1992; Boiler et al., 1989).


Introduction2

Introduction

  • diagnose VD several techniques have been developed, like the Hachinski scale (Hachinski & Lassan, 1974)

  • without the possibility of obtaining a correct differential diagnosis VD (Villardita, 1993; Gorelick & Roman, 1993; von Reutern, 1991).


Introduction3

Introduction

  • Artificial Intelligence, AI

  • complex problems in medical diagnosis can be approached. For example, pattern recognition in X-ray images (Boone et al., 1990a,b; Gross et al., 1990; Hallgren & Reynolds, 1992), biomedical signals analysis (Gevins & Morgan, 1988; Mamelak et al., 1991; Alkon et al., 1990; G~ibor & Seyal, 1992; Gfibor et al., 1993) and prediction and diagnosis problems(Casselman & Maj, 1990; Poli et al., 1991; Moallemi, 1991; Baxt, 1991).


Data collection training and test sets

Data collection: Training and Test sets

  • To carry out a differential diagnosis of AD and VD

  • Collection data as follow (Bolla et al., 1991; Fisher et al., 1990; Krall, 1983; Rovner et al., 1989):

    • how the problem started (i.e. sudden, or slow and progressive)

    • nature of the initial dysfunction (e.g. loss of memory, language alterations, problems to execute motor action, and the incapacity for recognizing objects, colors or situations)

    • Information about changes in personality and depressive symptoms


Data collection training and test sets1

Data collection: Training and Test sets

  • In addition, without a unique methodology to carry out the differential diagnosis of AD and VD

  • Findings generated by:

    • (a) different tests (e.g. physical and neurological exams, as well as blood tests)

    • (b) a psychological interview

    • (c) nutritional information

    • (d) an evaluation of the vascular disease


Data collection training and test sets2

Data collection: Training and Test sets

  • Demographic

    • patient's age, sex, civil state, patient's education, Occupation

  • Antecedents

    • smoke, alcoholism, hereditary antecedents, hypertension, history of depressive states, etc.

  • Symptoms and signs

    • illness time evolution, if the patient has orientation problems, changes in personality, problems with numerical calculus, language problems, or psychotic symptoms, etc.


Data collection training and test sets3

Data collection: Training and Test sets

  • Neurological and neuropsychological scales

    • patient's clinic history and a clinical exam

    • Loeb scale (Loeb, 1988; Cummings, 1985)

      • (in both scale was evaluated how the illness started)

    • The neuropsychological tests

      • (MMSE (Folstein et al., 1975);

      • Geriatric Depression Scale (Mattis, 1976; Diaz & Garcfa de la Cadena, 1993);

      • Common Activities Scale (Khachaturain, 1985; Diaz & Garcfa de la Cadena, 1993).


Data collection training and test sets4

Data collection: Training and Test sets

  • Electrophysiolog

    • EEG

    • P300

  • Neuroimaging analysis and other studies

    • Tomography(斷層掃描法) and Magnetic Resonance analyses(核磁共振) are used to valorize AD pathologies(DeLeon et al., 1980, 1983; Fox et al., 1975)


Data collection training and test sets5

Data collection: Training and Test sets

  • 58 paitents

  • National Institute of Neurology and Neurosurgery Manuel Velasco Sudrez

  • These cases were organized in three sets:

    • Set /----19 subjects diagnosed with VD.

    • Set II 16 subjects diagnosed with AD.

    • Set 111--23 subjects with diagnosis of dementia (AD or VD).


Network architecture and training parameters

Network architecture and training parameters

Learning rate 0.1

Momentum 0.1

Initial weights 0.3

Error value to stop the training 0.0000002

46 neurons

29 neurons

2 neurons


Results

Results

  • a neural network was trained during 65 hours in order to reach the minimum average error of 0.0000002

  • we presented the data corresponding to the 23 cases of the test set, and only obtained the correct classification of 19 cases, that is an 82.6% efficacy.


Results1

Results

  • Five networks classify correctly 21 of 23 test cases;

  • Five other networks classify correctly 20 of 23 test cases

  • The network trained with data from demographic records and scales studies, produces the best results, 22 of 23 test cases were classified correctly


New network

New Network

  • A correct classification was obtained for all 23 cases in the test set, that is, an efficacy of 100%.


Conclusions

conclusions

  • In medicine, there are many illnesses whose diagnosis is a very difficult task, and people are still searching for more efficient solutions

  • This automata performs quite well:

    • It presents a 100% efficacy

    • it helps improve the efficiency in the differential diagnosis of AD and VD

    • it helps to reduce costs


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