A convolutional neural network for high throughput screening of femoral stem taper corrosion.

ABSTRACT: Corrosion at the modular head-neck taper interface of total and hemiarthroplasty hip implants (trunnionosis) is a cause of implant failure and clinical concern. The Goldberg corrosion scoring method is considered the gold standard for observing trunnionosis, but it is labor-intensive to perform. This limits the quantity of implants retrieval studies typically analyze. Machine learning, particularly convolutional neural networks, have been used in various medical imaging applications and corrosion detection applications to help reduce repetitive and tedious image identification tasks. 725 retrieved modular femoral stem arthroplasty devices had their trunnion imaged in four positions and scored by an observer. A convolutional neural network was designed and trained from scratch using the ima

SUBMITTER: Codirenzi AM 

PROVIDER: S-EPMC10350735 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature