At present, mammography is the best non-intrusive way for breast cancer detection. Breast cancer risk assessment models are useful for personalizing mammography screening. It is well established that mammographic density (MD) is an independent and robust factor for breast cancer risk estimation. However, due to intra- and inter-reader variants in MD estimation, it is difficult to incorporate the MD factor into existing risk assessment models. This motivated us to develop the aforementioned software, MammoAid, for fully automatic and objective MD estimation. Further, we equipped this software with artificial intelligence (AI) techniques to emulate radiologists’ behaviour of shading the mammograms, to minimize intra- and inter-reader variants and meet the need of standardizing the MD assessment. Our software is capable to process both analogue and digital mammograms. We evaluated our software over 1000 analogue mammograms and the results showed that our software, when compared with doctor’s reference, achieved an accuracy of 87% and Pearson’s correlation coefficient of 0.95 in MD ratio estimation.