## 4 Discussion
Variations in the degree of gender dimorphism have been noted in different population groups, and the specific reason is still unclear. Cultural, environmental, and genetic factors have been known to influence tooth morphology. Furthermore, differences in study criteria, measurement methods employed, and racial and ethnic differences in different studies might contribute to varying results. As a result, population-specific data are needed for establishing reference data and prediction models for gender estimation. The method of gender prediction by means of canine indices is beneficial as it is economical, does not require complex equipment, involves simple mathematical calculation, and is convenient for the analysis of large samples [34]. Gender prediction using the standard canine index in the present study was more accurate in males compared to female counterparts; however, an overall accuracy of prediction was closer to 59% for both the right and left canines. Hence, the accuracy of gender prediction using SCI was considered poor in the present study. This study revealed that the difference in the mean values of right and left MCI between males and females was significant (\(p<0.05\)). Within the gender, the mean difference values of MCI between the right and left sides were found to be statistically significant both in males (\(p<0.001\); Wilcoxon \(Z=-6.695\)) and females (\(p<0.001\); Wilcoxon \(Z=-5.615\)). On the contrary, [PERSON] et al. [30] found no significant difference in the mean maxillary canine index of the right and left sides between the two genders or between two sides within the same gender. Discriminant analysis has been reported to be a reliable method to ascertain gender discriminant functions, whilst multivariate discriminant analyses generate higher discrimination percentages compared to the univariate method [4]. The multivariate discriminant function analysis with maxillary canine MD width and ICD, reported by [PERSON] et al. [16] on the Central Saudi population, permitted correct classification of 66.67% of males and 64.29% of females, with an overall accuracy of 65.48%. However, contrary to their finding, this study found that females (81.4%) were predicted slightly better than males (79.7%), and with a higher overall accuracy rate (80.5%). Another odontometric study on dental cast models found a high gender prediction accuracy rate of 99.8% in the population using stepwise discriminant functions [35]. Univariate discriminant analysis has been reported to be useful in circumstances where a limited number of teeth are available for forensic investigations. [PERSON] et al. [18], in their study on the Chilean population, found that the univariate discriminant functions of the right and left maxillary canines showed overall accuracy rates of 65% and 64%, respectively, which are comparatively lower than those in the current study. [PERSON] et al. [16] further concluded that ICD was more reliable than MD width in the correct classification of gender. Similarly, in the present study, discriminant function analysis showed that ICD was the most accurate predictor of gender (78%), followed by the right canine MD width (76.3%), whilst the left canine MD width showed 66.9% overall accuracy. Recent advances in the forensic and dental research fields have enhanced the gender determination methods with the development of newer computer-aided techniques. Application of machine learning methods in the age and gender estimation has been increasingly gaining attention as they are more accurate, less time-consuming, and cost-efficient compared to other methods [36; 37]. One limitation of the present study was that the sample used for evaluation of maxillary canines was selected from patients who sought dental care at a university dental clinical complex in Al-Ahsa, in the Eastern region of Saudi Arabia, and therefore generalization of results derived from this study in the Saudi population is limited. Further investigations involving larger samples from different ethnic populations should be conducted.