ARTIFICIAL NEURAL NETWORKING in Dentistry
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The document discusses clinical decision support systems (CDSS), particularly focusing on artificial neural networks (ANN) as non-knowledge-based adaptive systems that learn from past experiences. ANNs are inspired by biological neural systems and can process incomplete information, making them useful in various medical applications such as diagnostics and treatment planning in dentistry. Although they may initially seem complex compared to traditional methods, advancements in technology suggest that neural networks have the potential to serve as complementary tools in clinical decision-making.
ARTIFICIAL NEURAL NETWORKING in Dentistry
- 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.
Editor's Notes
- #2: FIRST STEP TO KNOWLEDGE IS TO KNOW THAT we are ignorant Knowledge in medical field is cgaracterized by uncetanity 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 bunction as early as 300 bc Till date ervous system is ot completely understood to humaikid
- #3: Decision support systems (DSS) are a specific class of computerized information systems that support business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions
- #4: Clinical decision-support systems (CDSSs) are computer programs that are designed to provide expert support for health professionals making clinical decisions.1These sys- tems can be used for diagnosis, prevention, treatment of health diseases, and future evaluation of the patien
- #11: The nodes are considered as computational units .Inputs can be fed into nodes which operate onto hem to give an output This procedure may be very simple (such as summing the inputs), or quite complex (a node might contain another network)
- #12: The nodes are considered as computational units .Inputs can be fed into nodes which operate onto hem to give an output This procedure may be very simple (such as summing the inputs), or quite complex (a node might contain another network)
- #13: The nodes are considered as computational units .Inputs can be fed into nodes which operate onto hem to give an output This procedure may be very simple (such as summing the inputs), or quite complex (a node might contain another network)
- #14: The nodes are considered as computational units .Inputs can be fed into nodes which operate onto hem to give an output This procedure may be very simple (such as summing the inputs), or quite complex (a node might contain another network)
- #15: The nodes are considered as computational units .Inputs can be fed into nodes which operate onto hem to give an output This procedure may be very simple (such as summing the inputs), or quite complex (a node might contain another network)
- #16: The nodes are considered as computational units .Inputs can be fed into nodes which operate onto hem to give an output This procedure may be very simple (such as summing the inputs), or quite complex (a node might contain another network)
- #17: This procedure may be very simple (such as summing the inputs), or quite complex (a node might contain another network)
- #18: quite complex (a node might contain another network)
- #20: The nodes can interact through their connections leading to a global behaviour of the network. This global behaviour is called as emergent. This ability of the network surpasses that of its elements, makingnetworks an exceptionally potent tool
- #26: 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 . 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