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

Expert Systems with Applications

ELS EVI E R Expert Systems with Applications 14 (1998) 219-225

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

Elizabeth Garcla-P6rez Subdireccidn General de Investigacidn, Instituto Nacional de Neurologfa y Neurocirugfa. Insurgentes Sur 3877, M~xico D.E, CP-14264, M~xico

Arturo Violante Divisi6n de Servicios Cl[nicos, lnstituto Mexicano de Psiquiatr£a, Calzada M~xico Xochimilco lOl, M~xico D.E, CP-14370, M~xico

Francisco Cervantes-P~rez t Departamento Acad~mico de Computacidn, lnstituto Tecnol6gico Autdnomo de Mdxico (ITAM), Av. Camino a Santa Teresa No. 930, Mdxico D.F.,

CP-10700, M~xico

Abstract

Differential diagnosis among different types of dementia, mainly between Alzheimer (AD) and Vascular Dementia (VD), offers great difficulties due to the overlapping among the symptoms, and signs presented by patients suffering these illnesses. A differential diagnosis of AD and VD can be obtained with a 100% of confidence through the analysis of brain tissue (i.e. a cerebral biopsy). This gold test involves an invasive technique, and thus it is rarely applied. Besides these difficulties, to get an efficient differential diagnosis of AD and VD is essential, because the therapeutic treatment needed by a patient differs depending on the illness he suffers. In this paper, we explore the use of artificial neural networks technology to build an automaton to assist neurologists during the differential diagnosis of AD and VD. First, different networks are analyzed in order to identify minimum sets of clinical tests, from those normally applied, that still allows a differential diagnosis of AD and VD; and, second, an artificial neural network is developed, using backpropagation and data based on these minimum sets, to assist physicians during the differential diagnosis of AD and VD. Our results allow us to suggest that, by using our neural network, neurologists may improve their efficiency in getting a correct differential diagnosis of AD and VD and, additionally, that some tests contribute little to the diagnosis, and that under some combinations they make it rather more difficult. © 1998 Elsevier Science Ltd. All rights reserved

1. I N T R O D U C T I O N

In the last years, because individuals' life expectations have increased all over the world, demential illnesses have become a main concern. 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). In Mexico, 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). A similar

* Due to circumstances beyond the publisher's control, this paper appears in print without author corrections.

Author for correspondence.

phenomenon has been observed with respect to brain vascular disease, one of the main causes of Vascular Dementia (VD), which has become the third cause of death in people of that age (Cummings & Benson, 1992). Additionally, due to the incapacity these illnesses produce in people, their impact on public health is considered as an issue of great importance.

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). A differential diagnosis between AD and VD presents great difficulties due to the similarities found among their symptoms characteristics, and in the clinical tests required for their classification. These problems can be overcome through the use of an invasive technique, i.e.

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220 E. Garcfa-Pdrez et al./ Expert Systems with Applications 14 (1998) 219-225

cerebral biopsy (McKhann et al., 1984). Even though this is a gold test (i.e. the only way to obtain the right diagnosis), it must be taken into account that invasive techniques present ethical problems, and that sometimes they may give rise to other medical complications. Thus, this technique is rarely used. Besides all of these difficulties, a correct differential diagnosis of AD and VD, as well as the identification of the right parameters to validate it, must be reached efficiently; mainly, because of the differences among the therapeutic treat- ments associated with each of these illnesses, and their possible natural development (e.g. VD can be associated to modifiable risk factors like systemic arterial hypertension). That is, the development of criteria for differentially diagnose VD and AD is fundamental for recognizing the underlying pathology, to start a proper therapy, as well as to determine the frequency and the prevalence of the disease.

The fact that the gold test to diagnose correctly AD and VD involves an invasive technique, has meant that, as an altemative way, neurologists analyze a huge amount of data, obtained from a series of clinical studies that should be practiced on the patient, before they may produce a proper diagnosis. For example, in trying to diagnose VD several techniques have been developed, like the Hachinski scale (Hachinski & Lassan, 1974), a set of observations generated from analyzing the patient's history, and data from clinical tests that suggest the presence of brain vascular disease, or enough damage to be the cause of the corresponding dementia. It must be pointed out that in some cases these studies are not conclusive, because a patient having AD may, at the same time, present a previous history of VD. In those cases, a vascular etiology is defined, but without the possibility of obtaining a correct differential diagnosis (Villardita, 1993; Gorelick & Roman, 1993; von Reutern, 1991). The same occurs in patients with dementia, where there are histological findings of AD and, at the same time, changes that are compatible with a vascular origin of the disease.

Additionally, Alzheimer is a progressive degenerative disease, characterized by alterations in memory, orientation, and in a variety of cognitive functions. These problems may appear at early stages, in people around 40 years old, but they are more frequently observed in people over 60 (Cummings & Benson, 1992; Friedland, 1993), where neuropathological changes have been recorded (Khachaturain, 1985; Selkoe, 1993; Mirsen et al., 1991) together with the coexistence of vascular factors in their etiology (O'Brien, 1992; Erkinjuntti et al., 1987). All of this complicates even more the differential diagnosis of AD and VD, and has required the analysis of other type of data, and of brain images (Boiler et al., 1989).

In the absence of specific indicators to diagnose AD or VD, and trying to avoid the use of studies involving invasive techniques, to find a clear differentiation

between both diseases becomes a very difficult task, because of the overlapping among those symptoms related to the cognitive impairment produced by AD and VD, it has been imperative to search for alternative methods, and technologies that allow us to establish reliable differential diagnoses for defining, as soon as possible, the proper treatment for the patient being analyzed.

In this quest, computer aided diagnosis in medicine has been done for quite some time now (Schwartz, 1970; Schwartz et al., 1987; Shortliffe, 1976; Kulikowki, 1980; Reggia & Tuhrim, 1985; Szolovitz et al., 1988), but it is not until recently, with the integration of different technologies (e.g. distributed artificial intelligence, neural networks, genetic algorithms and fuzzy logic), that 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). In general, this approach can be applied to situations where problem solving with traditional techniques becomes difficult, inefficient, or complicated.

Here, based on the dynamic properties displayed by artificial neural networks, and their proven ability for pattern recognition in complex situations (Widrow et al., 1994), our aim is two-fold: first, to show how by using artificial neural networks technology it is possible to determine minimum sets of tests, from those normally applied, that still allow a proper differential diagnosis of AD and VD; and, second, to build a neural computing automata, i.e. a distributed architecture of small neural nets trained with the backpropagation algorithm (Rumel- hart et al., 1986), to assist neurologist and non specialist physicians to obtain differential diagnoses of AD and VD with almost a 100% confidence.

2. DATA COLLECTION: TRAINING AND TEST SETS

To carry out a differential diagnosis of AD and VD, it must be taken into account that there are many possible causes that alter cognitive functions in an individual. Therefore, according to Eslinger and Damasio (1985), and Bayles (1991), it is important to get a detailed clinical history, including how the problem started (i.e. sudden, or slow and progressive), to be able to establish the 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 must be also included (Bolla et al., 1991; Fisher et al., 1990; Krall, 1983; Rovner et al., 1989), as well as on the type of drugs or medication that

E. Garcia-Pdrez et aL / Expert Systems with Applications 14 (1998) 219-225 221

the patient had been taking, in older people it has been observed that this fact may cause, or accelerate memory failure (Spiegel et al., 1981).

In addition, without a unique methodology to carry out the differential diagnosis of AD and VD, neurologists must integrate those results mentioned in the previous paragraph with findings generated by: (a) different tests (e.g. physical and neurological exams, as well as blood tests); (b) a psychological interview; (c) nutritional information; and (d) an evaluation of the vascular disease. Finally, to further increase this complexity, it must be considered that there are different criteria to produce a differential diagnosis of AD and VD. For example, to diagnose AD (Kukull et al., 1983; Kukull & Larson, 1990), two of the most frequently used criteria are:

• That from The Diagnostic and Statistical Manual of Mental disorders (DSMIII-R), which can be described as follows: there must be a confirmed diagnosis of dementia, with an insidious start, and it must be possible to exclude other causes of dementia by using data from the patient's clinic history, physical exams and other lab studies.

• The clinic diagnosis of AD developed by a working group of the NINCDS/ARDA, from the National Institutes of Health in the USA (McKhann et al., 1984). Their proposal may be resumed as:

Possible AD: Dementia diagnosed based on results of clinic exams that show progressive dysfunction of one, or more, cognitive processes, in the presence of systemic, or cerebral alterations not considered as the main cause of the dementia.

Probable AD: Early start of AD (subjects younger than 65 years old), diagnosed in the absence of systemic, or cerebral alterations.

Definitive AD: Confirmed clinical criteria for AD, based on data generated by invasive studies (i.e. biopsy or autopsy), as well as on neurological and psychiatric signs and symptoms.

Given the wide variety of possible causes, and the complexity of the analysis of the symptoms and signs presented by the patients, in their search to reach reliable differential diagnoses of AD and VD, neurologists depend on data obtained from a big set of exams and studies, which can been grouped as follows:

(a) Demographic--This group includes information related to the patient's age, sex, civil state, patient's education, and occupation.

(b) Antecedents---Here, information from the patient's clinical record is considered: smoke, alcoholism, hereditary antecedents, hypertension, history of depressive states, etc. These data are obtained by asking the patient, or some reactive, and they are

used to find risk factors for artherioscleroses. (c) Symptoms and signs~This group of data includes

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

(d) Neurological and neuropsychological scales--In trying to verify a diagnosis of vascular dementia, several methods have been designed, like Hachinski scales (Hachinski & Lassan, 1974), that are a set of observations from the patient's clinic history and a clinical exam, from which it is possible to suggest the presence of a brain-vascular illness, severe enough to be the cause of a dementia. Loeb scale represents another attempt for obtaining a differential diagnosis between VD and AD (Loeb, 1988; Cummings, 1985). In both scales, it is evaluated how the illness started (suddenly, or slowly), its evolution, the presence of specific symptoms and signs, and the history of arterial systemic hypertension. The neuropsychological tests used in our study are: (a) Mini Mental State Examination (MMSE), designed to valorize cognitive functions in a fast way (Folstein et al., 1975); (b) Geriatric Depression Scale, designed to evaluate depression in older people (Mattis, 1976; Diaz & Garcfa de la Cadena, 1993); (c) Common Activities Scale, used to evaluate social adaptive abilities in daily activities (Khachaturain, 1985; Diaz & Garcfa de la Cadena, 1993).

(e) Electrophysiology--In this group we have electroencephalogram (EEG), and P300 studies. On the one hand, EEG analysis results are normal at the beginning of the illness, and the background activity may become slower as the illness pro- gresses. On the other hand, evoked potentials studies, specifically P300, have been used to corroborate some findings in the neuropsychological analysis, because the shifting of the latency of the P300 component has been related to attention and memory tests (Patterson et al., 1983).

(f) Neuroimaging analysis and other studies--Tomog- raphy and Magnetic Resonance analyses are used to valorize AD pathologies, such as: signs of brain atrophy; increase in ventricle cavities, specially in the third ventricle, etc. It has been shown that the course of clinical deterioration in patients with dementia is closely correlated to these changes (DeLeon et al., 1980, 1983; Fox et al., 1975).

In order to build the corresponding neural net and taking into consideration all these types of data, a database was integrated with information from the clinical files of 58 well documented cases from the clinics for Brain Vascular Disease and Cognition, at the National Institute of Neurology and Neurosurgery Manuel Velasco Sudrez. These cases were organized in three sets:

222 E. Garc(a-PFrez et al. / Expert Systems with Applications 14 (1998) 219-225

• Set/----19 subjects diagnosed with VD. • Set II 16 subjects diagnosed with AD. • Set 111--23 subjects with diagnosis of dementia (AD or

VD).

The first two sets were used as the training set, whereas the third one was the test set.

3. N E T W O R K A R C H I T E C T U R E AND TRAINING PARAMETERS

Due to the complexity of the diagnosis, a nonlinear mapping was expected. Therefore, a three layers feedforward neural net was selected (see Fig. 1), and trained with the backpropagation learning algorithm, using the commercial simulator Neuroshell2, version 1.5 for windows. The number of characteristics for subject, to define the number of neurons in the input layer, was 46; while, the output layer was conformed by one neuron because the differential diagnosis might only be AD or VD. Based on an empirical formula, the simulator defines the number of elements in the hidden layer, which was 29. Different combinations of activation functions were tried and finally, a l inear activation function was used for the elements in the input layer, a logistic function for the hidden layer, and a hyperbolic

tangent function for the output neuron. In the simulations, for the network parameters, we

used the following values:

• rate learning, r/=0.1; • momentum, m = 0.1;

X 1 X 2 X 3 X4 X46

Input Layer

Hidden Layer

Output Layer

FIGURE 1. Neural network architecture. When using information from all groups of data, a three-layer feedforward network was trained with the Back, propagation learning algorithm. The Input layer had 46 elements, whereas the hidden layer had 29, and, because the diagnosis was AD or

VD, only one element comprised the output layer.

• initial weights value, o)=0.3; and • error value to stop the training, E= 0.0000002.

4. RESULTS

Using all the data (Demographic, Antecedents, Symp- toms, Scales, Electrophysiology, and Neuroimaging) of each subject, included in the training set, a neural network was trained during 65 hours in order to reach the minimum average error of 0.0000002. Then, 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.

Even though the results obtained with the trained network were good, in terms of the number of cases classified correctly (19 out of 23), our aim was to improve it until an efficacy as close as possible to the 100%. The number of variables used to diagnose between AD and VD is very large; apparently, neurologists have included new tests as new instrumentation has become available. Thus, we built a series of neural networks using different combinations of groups of data as input vectors, in order to analyze the level of importance associated to each data group during the differential diagnosis of AD and VD. The number of combinations of the six groups of data was 63; therefore, 63 neural nets were trained using the same architecture and parameters as described above, included the one trained with data from all groups, and the results obtained are in Table 1.

The efficacy of all 63 nets was determined by using data from the test set (Set III of 23 cases). From Table 1, it can be observed that 11 networks produce better results than the one trained with data from all six groups:

• Five networks classify correctly 21 of 23 test cases; • Five other networks classify correctly 20 of 23 test

cases; and • The network trained with data from demographic

records and scales studies, produces the best results, 22 of 23 test cases were classified correctly.

It must be pointed out that data from neurological and neuropsychological scales appear in all these networks, demographic information participates in six, electrophysiological and antecedents studies in five, whereas data from symptoms and neuroimaging only appear in three of the networks. In addition, in some cases, the combination of different groups of data improves the classification of the test set: nets trained with only data from antecedents and electrophysiological studies per- form poorly (12 of 23 cases classified correctly), a network trained with a combination of both groups of data improves the classification a little bit (13 of 23),,but when integrated with data from the scales group much better results are obtained, 20 of the 23 test set cases are classified correctly.

E. Garc[a-Pdrez et al./ Expert Systems with Applications 14 (1998) 219-225 223

Another important aspect in our study is to define in what cases the networks were wrong. This is shown in Table 2. Cases 17 and 20 were the more difficult to classify, four different networks misclassified them. Then, cases 5, 12, 14 and 24 follow in complexity, they were misclassified by three different networks; after- wards, we have cases 16 and 18, which were classified incorrectly by two networks; and, finally, only one network failed to classify cases 4 and 22.

In order to get the correct classification of all elements in the test set, an analysis of these 11 networks, and the cases they misclassify was conducted. In every case, there are more networks classifying them correctly than the ones misclassifying them; thus, if an odd number of these networks are run in parallel, and a neuron processor is placed to integrate their outputs into a majority function (see below), then all cases from the test set could be classified correctly. Networks 8, 10, and 11 (see Table 2), involve results from the analysis of neuroimaging, a costly and slow exam, therefore, we studied the situation where these networks were not considered for the resulting parallel architecture. Because failure of network 1 is included in the other three networks, its participation is redundant, and could also be eliminated. Thus, a set of seven neural networks (2, 3, 4, 5, 6, 7, and 9, in Table 2) was used to build the parallel architecture shown in Fig. 2, where their outputs are processed by a neuron-like element that carries out a majority function:

F(Y~,;0)= 1 if EY~->0 or 0 if ~EY~< 0;, for i=2,3,4,5,6,7,8,9

where 0 is equal to 4.

By using this distributed parallel architecture, a correct classification was obtained for all 23 cases in the test set, that is, an efficacy of 100%. Additionally, it should be noted that the required groups of data (i.e. demographic, neurological and neuropsychological scales, symptoms and signs, electrophysiological, and antecedents) can be obtained during the first days of medical consultation, and they represent no risk for the patient. Thus, using the resulting network to assist a neurologist, on a routine basis, in the differential diagnosis of AD and VD would help to solve one of the most pressing needs when seeing a patient for the first time: to diagnose correctly the type of dementia he/she has, and to be able to start immediately the proper treatment, especially for cases of reversible dementias where an etiological factor (thyroid, hormonal or phar- macological disturbance is present) can be observed and treated.

5. CONCLUSIONS

In medicine, there are many illnesses whose diagnosis is a very difficult task, and people are still searching for more efficient solutions. The sooner a patient starts the proper treatment, the better chances of getting him healthy again, or, as in the case of vascular dementia, slowing down the illness evolution, or, as in the case of AD, trying to make the rest of his life as comfortable as possible.

The development of new tools to build computer based machines to assist medical doctors in this quest has

Network l

Network 2

e

X7

e Network 7

YI

WI

F( Y; O)

FIGURE 2. A parallel distributed neural net was built by combining seven of the best 11 networks, the ones that classified correctly 20 or more cases from the Test Set. Different sets of data (X,, X2, X~, X~, Xs, X~, XT) were used to train the corresponding networks, and their outputs (Y, y=0,],...,7), were integrated by the output artificial neuron, which Implements a majority function, with which an efficacy of 100% was obtained. That is, all 23 cases from the Test Set were classified

correctly.

224 E. Garcfa-Pdrez et al. / Expert Systems with Applications 14 (1998) 219-225

been a ma in c o n c e m within the field of Cybemet ics (Weiner, 1948), and of Artificial Intel l igence (Barr & Fe igenbaum, 1982). In this paper, us ing tools based on an emergent technology (i.e. Neural Networks), we

described the design of a new automata to aid physicians in ident i fy ing complex patterns in patients suffering

i l lnesses (i.e. AD and VD) which are difficult to diagnose. This automata performs quite well: (a) it presents a 100% efficacy, that is, it produces the correct

classification of all cases in the test set; (b) it helps improve the efficiency in the differential diagnosis of AD and VD, because it uses data f rom tests that can be

carried out wi thin the first few days after the patient arrives at the clinic; and (c) it helps to reduce costs, our

analysis shows that differential diagnosis can be done without cons ider ing neuro imaging data, the most costly

studies. In addit ion, our analysis al lows us to suggest that some specific data, or the combina t ion of informat ion

from different groups of data, might not be part icipating

in the diagnosis , bu t rather they make it more difficult.

However , it must be pointed out that the neural

networks were trained, and tested, with the only 58 well

documented cases available at the momen t in the files of the cl inics for Brain Vascular Diseases, and for Cogni-

tion, at the Nat ional Insti tute for Neurology and Neurosurgery Manue l Velasco Sudrez. Because AD and VD might have different causes, it is highly probable that

future cases may have causes not considered in the deve lopment of our networks; therefore, whenever these

new cases appear it will be necessary to retrain the networks in order to include the new information.

Finally, based on our results, we may conclude that this automata will aid neurologists , and general practitio-

ners not acquainted with the especiality, in the decis ion mak ing process of d iagnos ing whether a dement ia l patient presents AD, or mult i - infarct VD. Even though more testing of the ne twork interact ing with the neurologist is required to conf i rm its utility, it is clear that those results discussed in this paper open a promis ing avenue

of research that will help improve the task of d iagnosing patients with some type of dementia. This l imitat ion will be explored in further studies.

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

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