In vivo studies in mechanobiology and mechanotransduction explained the importance of mechanical loading in promoting osteogenesis (new bone formation) and thus, in preventing the bone loss. The literature suggests that the cyclic loading parameters viz. loading cycles, strain and frequency regulate the extent of new bone formation. Nevertheless, the amount of regulation has not been defined. As a result, researchers have been trying a data driven approach to estimate the new bone formation by proposing different empirical models. The models proposed so far have mainly focused on some specific bone modelling parameters such as mineral apposition rate (MAR). The literature, however, suggests that there are equally important bone modelling parameters which are also influenced by the change in cyclic loading parameters. Therefore, the results obtained from earlier computer modelling studies remain incomplete. This paper presents an improved empirical model which attempts to establish a relation between bone modelling parameters mineral apposition rate (MAR) and mineralising surface (MS/BS), and cyclic loading parameters. The results indicate that the proposed model has better accuracy in terms of prediction as compared to the state-of-the-art models involving only one bone modelling parameter i.e., MAR. The model may be useful in designing the optimal loading regimen to induce a desired new bone response. Based on these outcomes, a better bio-mechanical intervention may be developed in future to check bone loss. INDEX TERMS Bone adaptation, mechanical loading, neural network, frequency, loading cycle.

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