For standard ‘off-the-shelf’ knee replacement procedures surgeons use X-ray images to aid implant selection from a limited number of models and sizes. This can lead to complications and the need for implant revision due to poor implant fit. Customised solutions have been shown to improve results but require increased preoperative assessment (Computed Tomography or Magnetic Resonance Imaging), longer lead times and higher costs which have prevented widespread adoption. To attain the benefits of custom implants, whilst avoiding the limitations of currently available solutions, a fully automated mass-customisation pipeline, capable of developing customised implant designs for fabrication via additive manufacturing from calibrated X-rays, is proposed. The pipeline uses convolutional neural networks to extract information from bi-planar X-ray images, point depth and statistical shape models to reconstruct the anatomy, and application programming interface scripts to generate various customised implant designs. The pipeline was trained using data from the Korea Institute of Science and Technology Information. Thirty subjects were used to test the accuracy of the anatomical reconstruction, ten from this dataset and a further twenty independent subjects obtained from the Osteoarthritis Initiative. An average root mean squared error of 1.00 mm was found for the femur test cases and 1.07 mm for the tibia. 3D distance maps of the output components demonstrated these results corresponded to well-fitting components, verifying automatic customisation of knee replacement implants is feasible from 2D medical imaging.