World Journal of Dentistry

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VOLUME 11 , ISSUE 2 ( March-April, 2020 ) > List of Articles


Are Artificial Neural Networks Useful for Predicting Overhanging Dental Restorations? A Cross-sectional Study

Hani T Fadel, Osama Abu-Hammad, Omar A Ghulam, Najla Dar-Odeh

Keywords : Algorithms, Dental restoration failure, Neural networks, Overhanging dental restoration, Prediction

Citation Information : Fadel HT, Abu-Hammad O, Ghulam OA, Dar-Odeh N. Are Artificial Neural Networks Useful for Predicting Overhanging Dental Restorations? A Cross-sectional Study. World J Dent 2020; 11 (2):99-104.

DOI: 10.5005/jp-journals-10015-1709

License: CC BY-NC 4.0

Published Online: 18-07-2020

Copyright Statement:  Copyright © 2020; The Author(s).


Aims: To predict the number of overhanging dental restorations (ODRs) using an artificial neural network (ANN) and determine the most important predictive variables. Materials and methods: Patient- and restoration-related data were used as input variables to construct two networks, with (network 1) and without (network 2) the number of secondary caries lesions (SCLs) as input data. Output data were the number of ODRs. Of the 502 participants, data of the first 100 were used to build/train the model. Those of the remaining 402 were used to test the model for prediction accuracy. Results: Model accuracy notably increased after training. Prediction of ODRs was more accurate in network 1. Allowing for an error of ±1, network 1 predicted the number of ODRs with an accuracy of 85.6%, whereas that of network 2 was only 82.1% accurate. The number of old fillings was the most important input variable, while gender was the least important. Conclusion: Within the study limits, the ANN model predicted ODRs with more than 85% accuracy. The number of old fillings was the most important predictive variable. Clinical significance: Making use of ANN analyses can help periodontists and general dentists predict the occurrence of ODRs, formulate effective treatment planning, and reduce patient discomfort and unnecessary costs.

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