1 research outputs found
Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength
The aim of this paper is to design a deep learning-based model to predict
proximal femoral strength using multi-view information fusion. Method: We
developed new models using multi-view variational autoencoder (MVAE) for
feature representation learning and a product of expert (PoE) model for
multi-view information fusion. We applied the proposed models to an in-house
Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345
African Americans and 586 Caucasians. With an analytical solution of the
product of Gaussian distribution, we adopted variational inference to train the
designed MVAE-PoE model to perform common latent feature extraction. We
performed genome-wide association studies (GWAS) to select 256 genetic variants
with the lowest p-values for each proximal femoral strength and integrated
whole genome sequence (WGS) features and DXA-derived imaging features to
predict proximal femoral strength. Results: The best prediction model for fall
fracture load was acquired by integrating WGS features and DXA-derived imaging
features. The designed models achieved the mean absolute percentage error of
18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using
linear models of fall loading, nonlinear models of fall loading, and nonlinear
models of stance loading, respectively. Compared to existing multi-view
information fusion methods, the proposed MVAE-PoE achieved the best
performance. Conclusion: The proposed models are capable of predicting proximal
femoral strength using WGS features and DXA-derived imaging features. Though
this tool is not a substitute for FEA using QCT images, it would make improved
assessment of hip fracture risk more widely available while avoiding the
increased radiation dosage and clinical costs from QCT.Comment: 16 pages, 3 figure