The objectives of this study were 1) to determine various hand dimensions and biomechanics measurements for Turkish dentistry students, 2) to evaluate the differences between males and females and between the right and left hand, and 3) to compare these data with similar data for other populations (Thai, Indian, Malaysian, British, Jordanian, Nigerian, Mexican, Bangladesh, and Vietnamese). The present study was undertaken to generate hand anthropometric and biomechanics data of 92 male and 73 female students studying at dentistry faculty. Sixty‐seven anthropometric and 26 biomechanics measurements were taken in both hands. The means, standard deviations, and percentile values were tabulated and compared with other populations. The results suggest that the Turkish female fingers are thinner than those of other females except Indians, and that the Turkish male fingers are wider than those of the other males. Furthermore, the results also suggest that the Turkish female strength in handgripping is greater than that of other females except British females, and the Turkish male handgrip strength is greater than that of other males except Americans. This study provides insights about Turkish hand dimensions and biomechanics; it can be a basis for future studies and the design of dental tools meant for the Turkish market. © 2012 Wiley Periodicals, Inc.

This study aimed to determine grip strength data for Turkish dentistry students and developed prediction models that allow: i) investigation of the relationship between grip strength and hand anthropometry using artificial neural networks (ANNs) and stepwise regression analysis, ii) prediction of the grip strength of Turkish dentistry students, and iii) assessment of the potential impact of hand anthropometric variables on grip strength. The study included 153 right-handed dentistry students, consisting of 81 males and 72 females. From 44 anthropometric and biomechanical measurements obtained from the right hands of the participants; five anthropometric measurements were selected for ANN and regression modeling using stepwise regression analysis. We included stepwise regression analysis results to assess the predictive power of the neural network approach, in comparison to a classical statistical approach. When the model accuracy was calculated based on the coefficient of determination (R 2), the root mean squared error (RMSE) and the mean absolute error (MAE) values for each of the models, ANN showed greater predictive accuracy than regression analysis, as demonstrated by experimental results. For the best performing ANN model, the testing values of the models correlated well with actual values, with a coefficient of determination (R 2) of 0.858. Using the best performing ANN model, sensitivity analysis was applied to determine the effects of hand dimensions on grip strength and to rank these dimensions in order of importance. The results suggest that the three most sensitive input variables are the forearm length, the hand breadth and the finger circumference at the first joint of digit 5 and that the ANNs are promising techniques for predicting hand grip strength based on hand breadth, finger breadth, hand length, finger circumference and forearm length.

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