English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate identification of the location the vertebra and corresponding pedicle is critical during pedicle screw insertion for percutaneous spinal fusion surgery. Currently, two dimensional (2D) fluoroscopy based navigation systems have extensive usage in spinal fusion surgery. Relying on 2D projection images for screw guidance results in high misplacement rates. Furthermore, fluoroscopy-based guidance exposes the surgical staff and patient to harmful ionizing radiation. Real-time non-radiation-based ultrasound (US) is a potential alternative to intra-operative fluoroscopy. However, accurate interpretation of noisy US data and manual operation of the transducer during data collection remains a challenge. In this work we investigate the potential of using multi-modal deep convolutional neural network (CNN) architectures for fully automatic identification of vertebra level and pedicle from US data. Our proposed network achieves 93.54% vertebra identification accuracy on in vivo US data collected from 27 subjects.

Ultrasound Guided Pedicle Screw Entry Point Identification for Spinal Fusion Surgery