Fully automated measurement on coronal alignment of lower limbs using deep convolutional neural networks on radiographic images.

ABSTRACT:

Background

A deep convolutional neural network (DCNN) system is proposed to measure the lower limb parameters of the mechanical lateral distal femur angle (mLDFA), medial proximal tibial angle (MPTA), lateral distal tibial angle (LDTA), joint line convergence angle (JLCA), and mechanical axis of the lower limbs.

Methods

Standing X-rays of 1000 patients' lower limbs were examined for the DCNN and assigned to training, validation, and test sets. A coarse-to-fine network was employed to locate 20 key landmarks on both limbs that first recognised the regions of hip, knee, and ankle, and subsequently outputted the key points in each sub-region from a full-length X-ray. Finally, information from these key landmark locations was used to calculate the above five parameters.

Resul

SUBMITTER: Meng X 

PROVIDER: S-EPMC9482267 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature