Data Collection for Each Model: Bovine Cervical Spine Puncture Accuracy

Xuanhuang Chen published a dataset on figshare in May 2026. It contains data from a study validating a structured light navigation system for minimally invasive cervical pedicle punctures. The data includes results from 124 Kirschner wire insertions into nine bovine spine models, with metrics on registration accuracy and trajectory deviations.

Use Cases

• Validate surgical navigation algorithms based on reported root mean square (RMS) and registration accuracy metrics.
• Benchmark the precision of minimally invasive puncture techniques using the reported entry and exit point deviations.
• Analyze factors affecting puncture accuracy based on the mention of bone surface factors influencing deviation.

Strengths

• Reports a 100% puncture accuracy rate (Grade 0 on the Neo classification) for 124 insertions.
• Provides specific quantitative results, including an average intraoperative RMS of 0.424 ± 0.12 mm and entry point deviation of 0.904 ± 0.419 mm.
• Data is associated with a published methodological study, providing context for the measurements.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small (104.7 KB), indicating limited scope.

Provenance

Source

figshare

Collection Method

Data collected from an experimental study using bovine cervical spine models, preoperative CT scanning, and intraoperative structured light scanning.

Freshness

Last updated 2026-05-11 17:24:31; freshness should be verified.