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

INTRODUCTION

Dental restorations or tooth fillings are customized three-dimensional additions to teeth, which can be made out of different materials and serve different clinical situations.¹ They form a mere replacement of the missing tooth structure rather than treatment for the multifactorial disease process of dental caries.² The quality of the dental restoration may be impacted by factors related to the patient, the dentist, and/or the restoration itself.³

Once in place, dental restorations may suffer from a number of mechanical or biological complications.⁴ These have adverse effects on the surrounding structures and can jeopardize the success of the restoration, leading to increased patient discomfort and wasting of resources.⁵ Overhanging dental restorations (ODRs) are noted as extensions of the restorative material beyond the confines of the cavity preparation.⁶ Overhanging dental restorations may be associated with bleeding, gingivitis, and periodontal tissue destruction.⁶ Possible reasons behind ODRs include inadequate use of matrix band system, suboptimal isolation, and/or improper handling of used restorative material.

Several mathematical models have been proposed to predict disease processes and complications in an attempt to actively prevent them and minimize treatment expenses. Artificial neural networks (ANN) are data-driven tools, used to analyze complex relationships. Unlike other models, data in ANN-based models are not fit to certain predefined conditions (e.g., linearity, normal distribution). Instead, experimental models are developed following training of available data.⁷ Usually, when the expert’s knowledge in a certain area is not substantial, ANN modeling may be of particular use. It works by adjusting of weights between the neurons for any input–output function approximation.⁸ In dentistry, ANN has been applied in predicting the occurrence of dental pain,⁹ recurrent aphthous ulcerations,¹⁰ tooth surface loss,¹¹ and several others.

To date, no studies have managed to predict the occurrence of ODRs mathematically to avoid associated periodontal tissue destruction, patient overtreatment, and unnecessary treatment costs. The study objectives were thus to predict the occurrence of ODRs using an ANN mathematical model in individuals with approximal dental restorations and to determine the most important predictive variables according to the model.

MATERIALS AND METHODS

RESULTS

The mean age of the 502 included participants was 38 ± 13 years (range: 15–87 years), 252 (50%) of which were females. Sixty-nine (14%) participants reported suffering from at least one medical condition. A high caries experience was observed in 28% of the sample, while normal caries experience was reported in 61%.

Tables 1 and 2 show topology and synaptic weights of the two constructed networks after training [see eqn (2)].

The accuracy of predicting ODRs was demonstrated in both networks (Table 3). Network 1 showed a prediction accuracy of 86% when allowing for a ±1 margin of error. However, this prediction accuracy was reduced to 82% in network 2 when SCLs were removed from the set of input data.

Figure 2 shows the average deviations of the two networks when predicting ODRs after every 10,000 training cycles up to 50,000 cycles. The figure demonstrates that the more the training, the less the deviations.

The relative importance of input data, i.e., independent variables, in network 1 was also evaluated and illustrated (Fig. 3). The number of old fillings in the mouth showed the highest importance (100%), while gender had the least (11%).

DISCUSSION

The aim of the current study was to predict the occurrence of ODRs in hospital attendees with approximal dental restorations. With selected patient- and restoration-related variables, the ANN model managed to predict the occurrence of ODRs with an accuracy of over 85%. This was in line with the high accuracy of ANN models reported in the available dental literature.¹⁵ In a model developed for the prediction of toothache, Kim et al. reported a predication accuracy of 80%.⁹ Similarly, Dar-Odeh et al. achieved an accuracy of 91% for predicting recurrent aphthous ulcerations.¹⁰ Such findings confirm the usefulness of ANN usage in dentistry and motivate further research and application.

The developed ANN utilized a number of input data to predict ODR occurrence, the highest importance of which being the number of old fillings in an individual. This may be understandable since amalgam, for example, is known to creep with time,¹⁶ while composite undergoes shrinkage,¹⁷ both of which leading to considerable changes in the restoration architecture and consequent accumulation of plaque and food debris.³ In such instances, and when adding suboptimal restorative clinical measures such as infrequent use of dental wedges, a restoration overhang might be inevitable.

The occurrence of ODRs has also been claimed to be related to diet.^18,19 Moradi-Lakeh et al. reported the consumption of considerable amounts of energy drinks following a large national survey in Saudi Arabia.²⁰ This might suggest an association with the occurrence of ODRs, as fizzy drinks cause tooth erosion, which may in turn contribute to an overhanging filling. However, dietary information was not collected in the current investigation; thus, such speculations would need future confirmation.

When adding SCLs as an input variable, the ANN model demonstrated a higher prediction accuracy for ODRs. This may be explained by the fact that SCLs lead to undermining of the tooth filling, where a combination of a caries lesion and an open margin is actually present.²¹ This would ultimately form a plaque retentive area, even though was not initially a true ODR. Accordingly, early management of secondary caries and underlying causes is considered a priority and should not be overlooked.

In a previous study by Ghulam and Fadel,²² individuals suffering from one or more medical conditions were observed to have more ODRs, whereas in the current investigation, medical status was of low input importance. This is explained by the fact that the previously used cluster analysis aimed to explain the observed distribution of current ODRs to direct interventional efforts and only utilized the few available data accordingly. In the present investigation, however, the formulated ANN models were meant to focus on future ODRs for preventive programs rather than prevalent ones and consequently required the utilization of a larger number of patient- and restoration-related input data.

The average deviation of the two ANN models was clearly reduced with the increasing number of training cycles. This was in line with the study by Xie et al., which utilized ANN modeling to determine the necessity for tooth extraction prior to orthodontic treatment.²³ In that study, the prediction accuracy increased from 80 to 100% following effective pretraining.

Allowing for a marginal error of ±1 ODR increased the accuracy of the constructed ANN models, although this in itself may be considered as a study limitation as it allows for a degree of uncertainty. However, the subject of ODRs detection remains a highly subjective matter and is dependent on the used methods and settings.⁶ Moreover, this points to the fact that the current investigation only involved radiographic evaluation and was not complemented with a clinical examination, possibly affecting the noted prevalence of ODRs.⁶ Nevertheless, utilizing radiographs to construct the proposed prediction models may prove to be of significant importance and practicality in large surveillance studies, especially when detailed clinical examination is not possible.

CONCLUSION

Within the limitations of this study, it can be concluded that ANN modeling predicted the occurrence of ODRs with more than 85% accuracy. The most important predictive factor was the number of old fillings, while the least important factor was gender.

CLINICAL SIGNIFICANCE

This study is the first to utilize a mathematical model using ANN in an attempt to predict the occurrence of ODRs. Making use of such analyses can help periodontists and general dentists predict occurrence of these clinical situations, formulate effective treatment planning, and reduce patient discomfort and unnecessary costs.

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