Segmenting/Pre-Processing Data from Bone Screw Thread-Stripping Tests.

Area of Science:

• Orthopaedic biomechanics
• Surgical instrumentation

Background:

• Optimal bone screw tightening is critical for fixation strength and stability.
• Over- or under-torquing can lead to implant failure and compromised patient outcomes.
• Existing model-based systems for optimal insertion torque require validation of combined functionalities.

Purpose of the Study:

• To pre-process data for validating a combined model-based system for optimal bone screw insertion torque determination.
• To establish a foundation for testing the integrated reverse and forward modeling steps.

Main Methods:

• Data segmentation based on specific recorded features.
• Testing the data pre-processing methodology on 50 screw-insertion datasets across 5 materials.
• Utilizing a model-based approach involving reverse and forward modeling steps.

Main Results:

• Successful segmentation of all 50 datasets using the defined parameters.
• The pre-processing methodology proved effective across diverse material types.
• This segmentation forms a basis for further data processing and system validation.

Conclusions:

• The developed data pre-processing steps are essential for validating the complete torque limit determination system.
• Successful validation of the integrated system has the potential to enhance patient outcomes in orthopaedic procedures.
• This work lays the groundwork for assessing the performance of automated torque control in bone screw insertion.