Abstract
Bone screws are used in many orthopaedic procedures. For low-density cancellous and osteoporotic bone, the torque margin for bone screw insertion can be low; over-tightening will strip the threads, while under-tightening may allow screw loosening over time. A model-based system of automatic individualised torque regulation has been proposed, however current efforts assume speed-independence. This publication addresses the lack of knowledge on the speed dependance of insertion torque. Therefore, a bone screw was inserted at various speeds between 2.5 and 25 rpm into 4 different densities of polyurethane foam synthetic bone. A statistically significant upwards trend was found for all materials (p < 0.05). These findings can be used to correct identified bone strength values from previous models, although investigating the physical cause may improve correction schemes.
Zusammenfassung
Knochenschrauben werden bei vielen orthopädischen Eingriffen verwendet. Insbesondere bei einer Kombination aus spongiösem und osteoporotischem Knochen mit geringer Dichte ist der Spielraum für das Drehmoment beim Einsetzen von Knochenschrauben gering; ein zu starkes Anziehen führt zur teilweisen Zerstörung des Gewindes, während ein zu geringes Anziehen dazu führen kann, dass sich die Schraube im Laufe der Zeit lockert. Es wurde be-reits ein modellbasiertes System zur automatischen, individuellen Bestimmung der Drehmomentregulierung vorgeschlagen, jedoch gehen die derzeitigen Bemühungen von einer Geschwindigkeitsunabhängigkeit aus. In dieser Arbeit wurde getestet, ob das Einschraubdrehmoment tatsächlich geschwindigkeitsunabhängig ist. Hierfür wurde eine Knochenschraube mit verschiedenen Geschwindigkeiten zwischen 2,5 und 25 U/min in 4 biomechanische Simulatoren aus Polyurethan unterschiedlicher Dichte eingebracht. Es wurde festgestellt, dass eine statistisch signifikante positive Korrelation zwischen steigender Geschwindigkeit und steigendem Drehmoment für alle Materialien (p < 0,05) bestand. Diese Ergebnisse können zur Korrektur der ermittelten Knochenfestigkeitswerte aus früheren Modellen verwendet werden, obwohl weitere Untersuchungen der physikalischen Ursache dieser Beziehung zu besseren Korrekturschemata führen könnten.
About the authors
Jack Wilkie was Born and raised in New Zealand. He attended the University of Canterbury after high school and received his Bachelor of Engineering with First Class Honours in Mechatronics Engineering in 2019. He is now studying towards his PhD at the Institute of Technical Medicine in Villingen-Schwenningen under the supervision of Professor Möller, in close co-operation with Professor Rauter in the Department of Biomedical Engineering at the University of Basel.
Georg Rauter studied mechanical engineering at TU-Graz and mathematical and mechanical modelling at MATMECA, Bordeaux. In 2014, he received his PhD in robotics from ETH Zurich. From 2014 to 2016 he was postdoc in rehabilitation robotics at ETH Zurich, University of Southern California, and University of Zurich. In 2016, he commercialized the gait rehabilitation robot the FLOAT in collaboration with the company Lutz Medical Engineering and the Spinal Cord Injury Center at the Balgrist, Zurich. Since 2016, he has headed the BIROMED-Lab as Asst. Prof. for Medical Robotics and Mechatronics at the DBE, University of Basel. Georg Rauter is IEEE member since 2011 and chair for IFToMM Switzerland since 2019.
Knut Möller studied computer science and human medicine in Bonn and received his PhD in computer science in 1991. Since 1998 he is a professor of Medical Informatics at the University Furtwangen and has been head of the Institute for Technical Medicine since 2010. He is also an adjunct professor in the Department of Mechanical Engineering at the University of Canterbury since 2020, and an associate professor in the Technical Faulty of the University of Freiburg since 2020. His main areas of research are medical informatics, medical imaging with focus on electrical impedance tomography, artificial neural networks in signal analysis and pattern recognition as well as physiological modelling for diagnostic and therapy optimization.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: Partial support by grants “CiD” and “Digitalisation in the OR” from BMBF (Project numbers 13FH5E02IA and 13FH5I05IA).
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Conflict of interest statement: Authors state no conflict of interest.
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Ethical approval: The conducted research is not related to either human or animal use
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