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

Bone screws must be appropriately tightened to achieve optimal patient outcomes. If over-torqued, the threads formed in the bone may break, compromising the strength of the fixation; and, if under-torqued, the screw may loosen over time, compromising the stability. Previous work has proposed a model-based system to automatically determine the optimal insertion torque. This system consists of a reverse-modelling step to determine strength, and a forward modelling step to determine maximum torque. These have previously been tested in isolation, however future work must test the combined system. To do so, the data must be segmented and pre-processed. This was done based on specific features of the recorded data. The methodology was tested on 50 screw-insertion data sets across 5 different materials. With the parameters used, all data sets were correctly segmented. This will form a basis for the further processing of the data and validating the combined systemClinical relevance: The system for torque limit determination must be tested in its entirety to properly asses its performance. This paper discusses some of the steps required to pre-process the data to make this assessment. If successful, this system may improve patient outcomes in orthopaedic surgery.

Investigating Torque-Speed Relationship of Self-Tapping Screws.

Correctly torquing bone screws is important to prevent fixation failures and ensure positive patient outcomes. It has been proposed that an automatic model-based method may be able to determine the patient-specific material properties of bone, and provide objective and quantitative torquing recommendations. One major part of developing this system is the modelling of the bone-screwing process, and the self-tapping screwing process in general. In this paper, we investigate the relationship between screw insertion torque (Nm) and speed of insertion (RPM). A weak positive correlation was found below approximately 30 RPM. Further research should focus on increasing the precision of the methodology, and this testing must be extended to ex-vivo animal bone testing in addition to the polyurethane foam substitute used here.Clinical relevance: To maximise the accuracy of torque recommendations, the model should account for all important factors. This study investigates and attempts to quantify the relationship between screw insertion speed and torque for later inclusion in modelling if significant.

Geometric Generalization of Self Tapping Screw Insertion Model.

Bone screws are used in orthopaedic procedures to fix implants and stabilise fractures. These procedures require care, as improperly torquing the screws can lead to implant failure or tissue damage, potentially requiring revision surgery or causing further disability. It was proposed that automated torque-limit identification may allow clinical decision support to control the screw torque, and lead to improved patient outcomes. This work extends a previous model of the screw insertion process to model complex thread geometries used for bone screws; consideration was made for the variable material properties and behaviours of bone to allow further tuning in the future. The new model was simulated and compared with the original model. The model was found to be in rough agreement with the earlier model, but was distinct, and could model thread features that the earlier model could not, such as the fillets and curves on the bone screw profile. The new model shows promise in modelling the more advanced thread geometries of bone screws with higher accuracy.Clinical relevance: This work extends a self tapping screw model to support complex thread shapes, as common in bone screws, allowing more accurate modelling of the clinically relevant geometries.

Quantifying Accuracy of Self-Tapping Screw Models.

Correct torquing of bone screws is important to prevent fixation failures and ensure positive patient outcomes. It has been proposed that an automatic model-based method may be able to determine the patient-specific material properties of bone, and provide objective and quantitative torquing recommendations. Models have been previously proposed for identifying the bone material properties, but have not been experimentally tested for accuracy. Here we used these models to perform parameter identification on experimental data using a variety of materials (rigid polyurethane foams) and screws. The identified values were then compared to the values from the datasheet, and matched with a reasonable accuracy for medium-density foam. It was found that for the lower-density foam, the model slightly under-predicted the strength, and for the highest density foam there was a large under-prediction. This suggests that with appropriate calibration, this method is good, but may only be applicable to lower-to-medium strength materials. More thorough testing is required to confirm this and determine the reliable density range.Clinical relevance: Accurate material property identification is required to provide effective torque recommendations for bone screws. This work quantifies the accuracy of two proposed models for material property identification.