ARTIFICIAL NEURAL NETWORKING. FIRST STEP TO KNOWLEDGE IS TO KNOW THAT we are ignorant Knowledge in medical field is characterized by uncertanity and vagueness Historically as well as currently this fact remains a motivation for the development of medical decision support system are based on fuzzy logics Greek philosopher visualized a basic model of brain function as early as 300 bc Till date nervous system is not completely understood to human kind.

1. Dr.ATHUL CHANDRA.M
2d year postgraduate

2. Decision support systems (DSS)

3. Decision support systems (DSS)
Clinical decision support system (CDSS)

4. There are two main types of CDSS:
1. Knowledge-Based and
2. Non Knowledge-Based.

5. 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 knowledge from past
experiences / examples

6. The ANN CDSS has the ability to cognitively process
incomplete information by guessing the lacking data and
improves with every use due to its adaptive learning system.

7. The ANN CDSS has the ability to cognitively process
incomplete information by guessing the lacking data and
improves with every use due to its adaptive learning system.
Additionally, ANN
systems do not require
large databases to store
outcome data

8. The ANN CDSS has the ability to cognitively process
incomplete information by guessing the lacking data and
improves with every use due to its adaptive learning system.
Greenwood stated that ANN can successfully execute
multitasking
Additionally, ANN
systems do not require
large databases to store
outcome data

9. Networks
“Divide and defeat”
Any complicated entity can be split into simple basic elements, so
that it can be easily processed. The simple elements can
also be assembled to form a complex system
These entities are nodes
and connection between
nodes these are
collectively called as
THE NETWORK

19. The connections resolve the information flow between nodes
unidirectional or bidirectional.
emergent.

20. The networks consider the nodes as ‘artificial neurons’.
Artificial neural networks (ANN) are inspired by the
biological neural system and its ability to learn through
examples.

21. Inputs are like synapsis
Weights are the strength of the signal
Activation of neurons is by a mathematical formula
Instead of following a group of well-defined rules specified by
the user, neural networks gain knowledge through intrinsic rules
obtained from presented samples

22. Inputs are like synapsis
Weights are the strength of the signal
Activation of neurons is by a mathematical formula
Instead of following a group of well-defined rules specified by
the user, neural networks gain knowledge through intrinsic rules
obtained from presented samples

23. 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 confirming the weights of an artificial neuron.

24. Algorithms 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

25. It can discriminate important patterns in input
information and respond with an appropriate output.
It deals with missing and uncertain input data, often
still giving the best decision.
It needs training, but can execute well even when
training has been undertaken with incomplete data
do not require a series of rules to be made explicit,
unlike other CDP

26. Applications of ANN in various
other fields of Dentistry
Diagnosis and differentiation of the subgroups
of temporomandibular internal derangements.
Investigation of the properties of dental
materials like ceramics
Identifying people at risk of oral cancer and pre-
cancerous lesions
Automated Dental Identification System (ADIS)
that addresses the problem of post- mortem
(PM) identification

27. Decision making for edentulous jaws
Dental age estimation
To diagnose aggressive periodontistis
clinical decision making on orthodontic
extractions
Predict the size of unerupted teeth

28. Clinical Challenges:
A good deal endeavor 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

29. 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.

30. References
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.

31. 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.