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

Authors

José Antonio Yáñez González-Cuéllar, Isabel Del Pilar Moscol Albanil, Carlos Ojeda, Francisco Franco-Martínez, Andrés Díaz Lantada, William Solórzano-Requejo

Published in

Computer methods and programs in biomedicine. Volume 285. Pages 109521. Jun 13, 2026. Epub Jun 13, 2026.

Abstract

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.

PMID:
42320193
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.

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