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. Customized 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-customization pipeline, capable of developing customized implant designs for fabrication via additive manufacturing from calibrated X-rays, is proposed. The proof-of-concept pipeline uses convolutional neural networks to extract information from biplanar X-ray images, point depth, and statistical shape models to reconstruct the anatomy, and application programming interface scripts to generate various customized 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 data set, and a further 20 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. Three-dimensional (3D) distance maps of the output components demonstrated these results corresponded to well-fitting components, verifying automatic customization of knee replacement implants is feasible from 2D medical imaging.