Chandra A et al IJRD ISSUE 2, 2014

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Artificial neural network –

A Recent Decision Supporting

System in Endodontics

Chandra A*, Rathinavel C*

* Department of Conservative Dentistry and Endodontics, Faculty of Dental Sciences, King George Medical

University, Lucknow. Uttar Pradesh. India

Address for correspondence: Dr Anil Chandra, Department of Conservative Dentistry and Endodontics, Faculty of Dental Sciences, King George Medical University, Lucknow- 226003 Uttar Pradesh. India. Mob : +919415029863 Email: ahanachandra@yahoo.com

Abstract : Decision Support System (DSS) is a computerized information system proposed to facilitate decision making in many fields

based on the knowledge, data, technologies and/or models. Artificial Neural network (ANN) is a type of DSS that can perform skilful

decisions and activities of the brain. They use various interconnected basic processors, equivalent to neurons, linked together to shape a

‘neural network’. External data can be used to train neural networks rather than applying a set of formal decision rules. ANN can assist

physicians to evaluate complex clinical data in a broad range of applications. Neural networks can help out with problems related to brain

functions like ‘visual and olfactory functions’, ‘modelling memory’ and ‘to control of robots that mimic organisms’. Recently this has been

employed as a diagnostic system in medicine and dentistry. This review aims at presenting the numerous uses of ANN in endodontics.

Keywords: Artificial neural network, decision supporting systems, locating apical foramen, proximal caries diagnosis, root fracture diagnosis.

INTRODUCTION

“The first step to knowledge is to know we are

ignorant.” (David Cecil)

The 21th century has experienced the rapid and

exponential advancement in the computer

technology and their successful application in the

medical field. Plato, a well-known Greek

philosopher, visualized a basic model of brain

function as early as 300 BC. Even today, the

nervous system is not completely understood.

Memory and many other activities of the brain are

based on certain specific model of the

interconnections among neurons. Efforts taken to

study the brain functions by computer simulation

could not succeed until the computing ability

developed many complex networks.

Clinical decision support system (CDSS) is an

interactive decision support system (DSS), which

is intended to aid physicians and other health

professionals with decision making tasks like

determining diagnosis of patient data. [1]

Clinical

Decision Support systems relate health

observations with health knowledge to manipulate

health choices by clinicians for enhanced health

care. There are two main types of CDSS: 1.

Knowledge-Based and 2. Non Knowledge-Based.

Artificial Neural Network (ANN) is a non

knowledge-based adaptive CDSS that applies a

form of artificial intelligence, also known as

machine learning. This enables the system to gain

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knowledge from past experiences / e

discerns patterns in clinical data. B

input from experts, ANN obviates

program the system which is one

advantages. The ANN CDSS has th

cognitively process incomplete info

guessing the lacking data and im

every use due to its adaptive learn

Additionally, ANN systems do not r

databases to store outcome dat

associated probabilities. Artifi

networks are able to execute lots of

successfully. A comprehensive rev

work was carried out by Greenwoo

networks are known as multi-layer

that can resolve multifarious problem

the spotlight of passionate research

computational level and as a system

day-to-day relevance. [3]

This netw

definite uses in Endodontics, and the

this article will focus on the same.

Networks

“Divide and defeat” is one of the best

work out many knotty jobs. Any

entity can be split into simple basic

that it can be easily processed.

elements can also be assembled

complex system. [4]

This can be a

with networks. There are many forms

which contain: a set of nodes and

between nodes.

The nodes are considered as computa

Inputs can be fed into nodes which

them to give an output. This proced

very simple (such as summing the

quite complex (a node might con

network). The connections re

information flow between nodes (figu

can be unidirectional or bidirectiona

can interact through their connections

global behaviour of the network.

behaviour is called as emergent. Th

the network surpasses that of its elem

networks an exceptionally potent tool

IJRD

/ examples and

By furnishing

s the need to

e of the main

the ability to

nformation by

improves with

arning system.

t require large

ata with its

ificial neural

of computation

eview of this

ood. [2]

These

er perceptrons

lems. They are

rch at a basic

m for realistic

twork has got

e remainder of

est strategies to

y complicated

ic elements, so

. The simple

d to form a

accomplished

ms of networks

d connections

utational units.

ich operate on

cedure may be

the inputs), or

ontain another

resolve the

igure 1) which

nal. The nodes

ns leading to a

. This global

This ability of

ments, making

ol. [5]

Networks are used to m

applications in computer

physics, biochemistry,

telecommunications and m

Figure 1. Real and artifici

neural body is represented in

as a circle, and is called a

represented as lines connect

weights.

Artificial Neural Netw

The networks consider t

neurons’. Artificial neura

inspired by the biological

ability to learn through

following a group of well

by the user, neural netw

through intrinsic rules ob

samples. [6]

An artificial ne

model inspired by the

obtain impulses through s

dendrites or the membran

the impulses received

(surpasses the threshold),

and transmits a signal t

signal can be transmitted

can also excite other neuro

The complexity of rea

abstracted when modell

They consist of inputs (lik

manifold by weights (stre

signals), and then worked

function which determine

neuron. Another function

identity) computes the o

neuron. ANNs combine ar

ISSUE 2, 2014

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model a broad array of

er science, mathematics,

economics, sociology,

many other fields.

icial neural networks. The

in artificial neural networks

a node. The synapses are

cting nodes, and are called

etworks:

the nodes as ‘artificial

ral networks (ANN) are

cal neural system and its

h examples. Instead of

ell-defined rules specified

etworks gain knowledge

obtained from presented

neuron is a computational

natural neurons. They

synapses located on the

ane of the neuron. When

d are strong enough

), the neuron gets excited

though the axon. This

d to another synapse and

rons.

real neurons is highly

elling artificial neurons.

like synapses), which are

trength of the respective

ed out by a mathematical

nes the activation of the

ion (which may be the

output of the artificial

artificial neurons in order

Chandra A et al IJRD ISSUE 2, 2014

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to process information. Larger the magnitude of

the weight of an artificial neuron is, the stronger

the input which is multiplied by it will be.

Weights can also be negative, so it is said that the

signal is inhibited by the negative weight. The

computation of the neuron differs with the

weights. Desired output for a specific input can be

obtained by conforming the weights of an

artificial neuron. It is difficult to determine all the

necessary weights manually when there is an

ANN of multiple neurons. Algorithms can be

found to regulate the weights of the ANN to

achieve the desired output from the network. This

process is known as learning or training. A

trained artificial neural network can categorize

significant patterns in the input data and gives a

proper output. The first neural model was given

by McCulloch and Pitts (1943) after which

numerous models have been developed. It has

been demonstrated that they can be successfully

applied in various areas of medicine such as:

diagnostic systems, biomedical analysis, image

analysis and drug development. [7-11]

Non medical neural network-based classification

systems include a system for classification of

sonar targets in submarine warfare, [12]

which

performed as well as human experts, and a system

for handwriting recognition, [13]

which was able to

identify correctly hand-written numbers with 98%

accuracy. Some work has also been undertaken on

systems that can hypothesize and predict

outcomes based on the hypotheses formed.

A single neuron, whether an artificial construct or

biological, is useless without interconnections in a

network. When connected together, the resultant

network can have important and powerful

properties:

(1) A trained neural network can discriminate

important patterns in input information and

respond with an appropriate output. For example,

a neural network trained to recognize pathology

on radiographic images, such as that by Gross et

al, [14]

could have many applications in dental

radiology.

(2) Neural networks can deal with missing and

uncertain input data, often still giving the best

decision.

(3) Neural networks need training, but can

execute well even when training has been

undertaken with incomplete data.

(4) Neural networks do not require a series of

rules to be made explicit, unlike other decision

supporting programs; rule derivation by

questioning an expert is a difficult and imprecise

process.

ANN for the Diagnosis of Proximal

Caries:

Devito et al trained an artificial intelligence model

(a multilayer perceptron neural network) to make

a radiographic diagnosis of proximal dental

caries. [15]

They compared bite-wing radiographs

of 160 extracted human teeth by means of

receiver operating characteristic (ROC) analysis

achieved with and without the use of the network.

The radiographic findings were fed into the neural

network. The respective teeth were sectioned and

examined under an optical microscope which is

the gold standard diagnostic tool for dental caries.

This helped to train the neural network in

diagnosing caries based on the radiographic

findings. They split the data into three subgroups

for training, test, and cross-validation to evaluate

the network’s ability to generalize. The receiver

operating characteristic (ROC) curve was drawn

with a Matlab function and the area under this

curve allowed comparison of efficacy between

network and examiner diagnosis. Neural network

got the ROC curve area higher than that of the

radiographic counterparts indicating an improved

sensitivity (39.4%) in proximal caries diagnosis.

ANN in Working Length

Determination:

Saghri et al proved that the information obtained

from radiographs using feature-extracting method

can be utilized and processed by neural networks

to successfully locate the minor apical foramen

(AF). [16]

They fitted the extracted teeth in the

alveolar socket of dried skull. They prepared the

Chandra A et al

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access cavity and radiograph wa

determine the working length. A

radiograph was also taken after re

tooth from the socket and this data

train the neural network (Figure 2).

to the minor foramen, they classified

of the tip into of the file into: bey

(long), within the root canal (short) an

minor AF (exact). With the help of O

the outline of the tooth on the rad

extracted out of it based on differen

scales. The length of the tooth was

‘MATLAB’. Perceptron uses these d

tooth and determines the reliability of

length measurement. They concluded

than 90% of the cases ANN precisely

apical foramen.

Saghiri et al also assessed the accurac

finding the exact working length

cadaver models thereby replicate

condition. [17]

They followed the simil

as the previous study and in ad

evaluated the exact position of

stereomicroscopically after extraction

confirmed that ANN can definitely

apical foramen in a higher numb

compared to conventional radiographi

Figure 2: ANN for locating apical foram

the software for training the backpropag

ANN in locating the apical foramen.

IJRD

was taken to

“subtracted”

removing the

ta was used to

). With respect

ed the position

eyond the AF

and just at the

f Otsu method,

adiograph was

rences in grey

as found using

details of the

of the working

ed that in more

ely located the

acy of ANN in

th in human

e the clinical

ilar procedure

addition they

the file tip

ion. [16]

It was

ely locate the

ber of cases

hic method.

amen.A snap of

agation type of

ANN for Detection

Fracture:

Kositbowornchai et al cre

for the diagnosis of verti

the images of intraoral d

appraised the sensitivity

including the effect of th

distribution. [18]

Digital rad

used to instruct the art

(Figure 3). The training

processed using greyscale

through the root. Then th

decrease the greyscale var

input data of the neural net

of the neural network

diagnostic test. The fina

ANN has greater sensitiv

comparable specificity

confirming vertical root fra

Figure 3: ANN for diagnosis

user interface for root fractur

Various Other

ANN in Dentistry:

1. Diagnosis and di

subgroups of tempo

derangements. [19]

2. Investigation of the

materials like ceramic

3. Treatment planning

molars. [21]

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n of Vertical Root

created a neural network

rtical root fractures from

l digital radiography and

y of the neural network

the alteration in function

radiographic images were

artificial neural network

g and tested data were

ale data per line passing

they were normalized to

ariance and introduced as

network. The performance

k was judged using a

nal conclusion was that

tivity (97.2– 98.0%) and

ty (60.0–90.5%) for

fractures.

sis of root fracture. Graphic

ture diagnosis.

Applications of

differentiation of the

poromandibular internal

the properties of dental

ics. [20]

ng for impacted third

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4. Identifying people at risk of oral cancer and

pre-cancerous lesions. [22]

5. Automated Dental Identification System

(ADIS) that addresses the problem of post-

mortem (PM) identification. [23]

Clinical Challenges:

A good deal endeavour has been put forth by

medical institutions and software companies to

construct viable CDSSs to cover all facets of

clinical tasks. But, due to the convolution of

clinical workflows and the initial high time

consumption, the institution deploying the support

system must make certain that the system

becomes a fluid and an integral part of the

workflow. The ANN systems develop their own

modus operandi for weighting and aggregating

data based on the statistical recognition patterns

over time which may be difficult to interpret and

doubt the system’s reliability in few cases.

Conclusion:

Neural networks initially give the impression of

complexity as we are accustomed to the

traditional ways to resolve decision making

problems. With the help of today’s technological

advancements, through little practice networks

enforced to get pragmatic solutions in diagnosis

as well as treatment planning in dentistry. Neural

network is a significant tool in the course of

warranting various concerns and must be the

focus of advance research. Though neural

network may not be able to substitute the

conventional methods in some cases, but for an

emerging list of applications, the neural network

will potentially act as an alternative or a

complementary to the existing techniques.

Reference:

1. Kensaku Kawamoto, Caitlin A Houlihan, E

Andrew Balas, David F Lobach.. Improving

clinical practice using clinical decision

support systems: a systematic review of trials

to identify features critical to success. BMJ

2005; 330: 765-73

2. Greenwood D. An overview of neural

networks. Behav Sci 1991; 36: l-33.

3. Crick F. The recent excitement about neural

networks. Nature 1989; 337: 129-32

4. Brickley MR, Shepherd JP, Armstrong RA:

Neural networks: A new technique for

development of decision support systems in

dentistry. J Dent 1998; 26: 305-9.

5. Gant, V., Rodway, S., & Wyatt, J. Artificial

neural networks: Practical considerations for

clinical applications. Cambridge: Cambridge

University Press 2001; 329–56.

6. Stevens, R. H. and Najafi, K. Artificial neural

networks as adjuncts for assessing medical

students problem solving performances on

computer based simulations. Comput Biomed

Res 1993, 26, 172-187.

7. Reggia JA: Neural computation in medicine.

Artif Intell Med 1993; 5:143-7.

8. Holst H, Mare K, Jarund A, Astrom K,

Evander E, Tagil K et al. An independent

evaluation of a new method for automated

interpretation on lung scintigrams using

artificial neural networks. Eur J Nucl Med

2001; 28:33–8.

9. Mango, L. J., Computer assisted cervical

cancer screening using neural networks.

Cancer Lett 1994; 77: 155-62.

10. P. A. Maiellaro, R. Cozzolongo, and P.

Marino: Artificial Neural Networks for the

Prediction of Response to Interferon Plus

Ribavirin Treatment in Patients with Chronic

Hepatitis C. Curr Pharm Design 2004; 10:

2101-09.

11. Okumura E, Kawashita I, Ishida T.

Computerized analysis of pneumoconiosis in

digital chest radiography: effect of artificial

neural network trained with power spectra. J

Digit Imaging 2011; 24:1126–32.

12. Gorman R, Sejnowoski T. Learned

classification of sonar targets using a

massively parallel network. IEEE

Chandra A et al IJRD ISSUE 2, 2014

Downloaded from www.jrdindia.org - 42 -

Transactions on Acoustics, Speech and Signal

Process, 1988; 36: 1135-40.

13. Burr, D. A neural network digit recogniser.

Proceedings of the 1986 IEEE International

Conference on Systems, Man and

Cybernetics, 1986; 2: 1621-25.

14. Gross G, Boone J, Greco-Hunt V, Greenberg,

B. Neural networks in radiographic diagnosis:

II interpretation of neonatal chest radiographs.

Invest Radiol 1990; 25: 1017-102.

15. Devito, Barbosa, Filho. An artificial

multilayer perceptron neural network for

diagnosis of proximal dental caries. Oral Surg

Oral Med Oral Pathol Oral Radiol Endod

2008; 106:879-84.

16. Saghiri MA, Asgar K, Boukani KK. A new

approach for locating the minor apical

foramen using an artificial neural network. Int

Endod J 2012; 45: 257–65.

17. Saghiri MA, Garcia-Godoy F, Gutmann JL.

The Reliability of Artificial Neural Network

in Locating Minor Apical Foramen: A

Cadaver Study. J Endod 2012; 38 (8): 1130-4.

18. Suwadee Kositbowornchai, Supattra

Plermkamon, Tawan Tangkosol. Performance

of an artificial neural network for vertical root

fracture detection: an ex vivo study. Dent

Traumatol; 2012first published online. doi:

10.1111/j.1600-9657.2012.01148.

19. Bas B, Ozgonenel O, Ozden B et al. Use of

artificial neural network in differentiation of

subgroups of temporo-mandibular internal

derangements: a preliminary study. J Oral

Maxillofac Surg. 2012; 70 (1): 51-9.

20. Jasenka Zivko-Babic et al. Estimation of

chemical resistance of dental ceramics by

neural network. Dent Mater 2008; 24: 18–27.

21. Brickley, M. R., Shepherd, J. P. Performance

of a computer simulated neural network

trained to make lower third molar treatment

planning decisions. Med Decis Making 1996;

16: 153-60.

22. Speight, P. M., Elliott, J., Downer, M.,

Zakrewska, J. The use of artificial intelligence

to identify people at risk of oral cancer and

pre-cancer. Brit Dent J.1995; 179: 383-87.

23. D.E.M. Nassar, H.H. Ammar. A neural

network system for matching dental

radiographs. Pattern Recognition 2007; 40: 65

– 79.

How to cite this article:

Chandra A, Rathinavel C. Artificial neural

network – A Recent Decision Supporting

System in Endodontics. IJRD 2014;3(2):37-

42.