Dual-CNN-based AI framework for patient-specific design of short femoral stems

This study introduces an AI-driven methodology for the personalized design of short femoral stems in total hip arthroplasty, addressing the challenge of stress shielding that compromises long-term implant survival. To improve pre-surgical implant suitability, a dual-input convolutional neural network (dual-CNN) was developed to predict the shielding directly from CT-like cross-sectional images that simultaneously capture femoral anatomy and stem geometry. A digitally generated dataset based on two segmented femurs and 392 stem designs was used for training and validation, while a third unseen femur assessed generalization. The influence of different dataset configurations was analyzed, with the combined dataset yielding the most accurate and robust predictions. The dual-CNN outperformed both single-anatomy models and a previously published random forest approach, reducing mean absolute error by approximately 30% and confirming the benefits of anatomically informed, image-based inputs. These findings demonstrate that the proposed model offers an efficient and scalable alternative to finite element analysis for evaluating stress/strain shielding and optimizing patient-specific short femoral stem designs.