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Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks.

Al Haidan A, Abu-Hammad O, Dar-Odeh N - Comput Math Methods Med (2014)

Bottom Line: The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer.The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model.Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%.

View Article: PubMed Central - PubMed

Affiliation: College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia.

ABSTRACT
Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

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Related in: MedlinePlus

Sums of deviations: before training, then at 1000 consecutive training cycles till 30000, and then at 50000 training cycles. Deviations decline with more training.
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fig5: Sums of deviations: before training, then at 1000 consecutive training cycles till 30000, and then at 50000 training cycles. Deviations decline with more training.

Mentions: Figure 5 also shows how deviations were reduced with more training of the network.


Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks.

Al Haidan A, Abu-Hammad O, Dar-Odeh N - Comput Math Methods Med (2014)

Sums of deviations: before training, then at 1000 consecutive training cycles till 30000, and then at 50000 training cycles. Deviations decline with more training.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4120478&req=5

fig5: Sums of deviations: before training, then at 1000 consecutive training cycles till 30000, and then at 50000 training cycles. Deviations decline with more training.
Mentions: Figure 5 also shows how deviations were reduced with more training of the network.

Bottom Line: The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer.The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model.Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%.

View Article: PubMed Central - PubMed

Affiliation: College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia.

ABSTRACT
Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

Show MeSH
Related in: MedlinePlus