title: Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks.
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.
authors.affiliation: College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia.
authors.forename: Ali
authors.initials: A
authors.lastname: Al Haidan
authors.affiliation: College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia.
authors.forename: Osama
authors.initials: O
authors.lastname: Abu-Hammad
authors.affiliation: College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia.
authors.forename: Najla
authors.identifier: 0000-0002-6577-2373
authors.initials: N
authors.lastname: Dar-Odeh
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003743:Dental Enamel / Q000473:pathology; D004032:Diet; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012907:Smoking / Q000009:adverse effects*; D012984:Software; D013499:Surface Properties; D011795:Surveys and Questionnaires; D014070:Tooth / Q000473:pathology*; D055815:Young Adult
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