To develop and validate a deep learning–based method for automatic quantitative analysis of lower-extremity alignment. In this retrospective study, bilateral long-leg radiographs (LLRs) from 255 patients that were obtained between January and September of 2018 were included. For training data (n 109), a U-Net convolutional neural network was trained to segment the femur and tibia versus manual segmentation. For validation data (n 40), model parameters were optimized. Following identification of anatomic landmarks, anatomic and mechanical axes were identified and used to quantify alignment through the hip-knee-ankle angle (HKAA) and femoral anatomic-mechanical angle (AMA).