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Inferring material properties in robotic bone drilling processes

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Recent innovations in robotics have enabled the development of automatic bone drilling tools which allows surgeons to improve the precision of their surgical operations. However, these tools still lack valuable tactile information about the material properties of the bone, preventing surgeons from making decisions while operating. The aim of this work is to explore whether robotic drilling tools can infer bone condition on the basis of certain key measures, particularly thrust force. Methods: To infer material properties in robotic bone drilling processes 1) a complete database of experimental operations with an automatic bone drilling tool is implemented and 2) binary logistic regression models are developed to estimate the type of material from the observed values (mainly the central tendency of the thrust force). This work compares three different materials: bovine bone specimens, porcine bone specimens and FullCure 720, which is a general-purpose resin with, a priori, much less feed resistance. The DRIBON automatic bone drilling tool developed at CEIT is used for the experiments. Results: The classification matrices derived using the logistic models show that it is possible to recognize a bovine bone vs. a porcine bone with a relatively high success rate rate (approximately 90%). In contrast, it is possible to recognize bone material vs. another material (in our case a resin) with a 100% of success. These results are successfully implemented in a new hand-held version of DRIBON. Conclusions: We propose a method and devise a novel hand-held tool which show that robotic systems can effectively infer bone material properties.
Rocznik
Strony
109--118
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
  • University of Navarra, TECNUN, School of Engineering, San Sebastián, Spain, jjgil@tecnun.es
autor
  • CEIT, Materials and Manufacturing Division, San Sebastián, Spain
  • CEIT, Materials and Manufacturing Division, San Sebastián, Spain
Bibliografia
  • [1] ACCINI F., DÍAZ I., GIL J.J., Bone recognition during the drilling process, 6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 2016, 305–310, Singapore.
  • [2] ACCINI F., DÍAZ I., GIL J.J., Using an admittance algorithm for bone drilling procedures, Computer Methods and Programs in Biomedicine, 2016, 123 (1), 150–158.
  • [3] AKAIKE H., Information theory and the maximum likelihood principle, 2nd International Symposium on Information Theory, 1973, 267–281, Budapest, Hungary.
  • [4] ALLOTTA B., BELMONTE F., BOSIO L., DARIO P., Study on a mechatronic tool for drilling in the osteosynthesis of long bones: tool/bone interaction, modeling and experiments, Mechatronics, 1996, 6 (4), 447–459.
  • [5] AUGUSTIN G., ZIGMAN T., DAVILA S., UDILLJAK T., STAROVESKI T., BREZAK D., BABIC S., Cortical bone drilling and thermal osteonecrosis, Clinical Biomechanics, 2012, 27 (4), 313–325.
  • [6] BASIAGA M., PASZENDA Z., SZEWCZENKO J., KACZMAREK M., Numerical and experimental analyses of drills used in osteosynthesis, Acta of Bioengineering and Biomechanics, 2011, 13 (4), 29–36.
  • [7] BOIADJIEV G., KASTELOV R., BOIADJIEV T., KOTEV V., DELCHEV K., ZAGURSKI K., VITKOV V., Design and performance study of an orthopaedic surgery robotized module for automatic bone drilling, The International Journal of Medical Robotics and Computer Assisted Surgery, 2013, 9 (4), 455–463.
  • [8] COULSON C., REID A., PROOPS D., A cochlear implantation robot in surgical practice, 15th International Conference on Mechatronics and Machine Vision in Practice, 2008, 173–176, Auckland, New Zealand.
  • [9] DÍAZ I., GIL J.J., LOUREDO M., Bone drilling methodology and tool based on position measurements, Computer Methods and Programs in Biomedicine, 2013, 112 (2), 284–292.
  • [10] FAWCETT T., An introduction to ROC analysis, Pattern Recognition Letters, 2006, 27(8), 861–874.
  • [11] GLAUSER D., FLURY P., VILOTTE N., C.W., B., Conception of a robotic dedicated to neurosurgical operations, Fifth International Conference on Advanced Robotics, 1991, 899–904, Pisa, Italy.
  • [12] HSU Y.-L., LEE W.-Y., LIN H.-W., A modular mechatronic system for automatic bone drilling, Biomedical Engineering, Applications, Basis and Communications, 2001, 13 (4), 168–174.
  • [13] KASTELOV R., BOIADJIEV G., BOIADJIEV T., DELCHEV K., ZAGURSKI K., GUEORGUIEV B., Automatic bone drilling using a novel robot in orthopedic trauma surgery, Journal of Biomedical Engineering and Informatics, 2017, 3 (2), 62.
  • [14] LABELLE D., BARES J., NOURBAKHSH I., Material classication by drilling, 17th International Symposium on Automation and Robotics in Construction, 2000.
  • [15] LEE W.-Y., SHIH C.-L., Force control and breakthrough detection of a bone drilling system, IEEE International Conference on Robotics and Automation, 2003, 1787–1792, Taipei, Taiwan.
  • [16] LEE W.-Y., SHIH C.-L., Control and breakthrough detection of a three-axis robotic bone drilling system, Mechatronics, 2006, 16, 73–84.
  • [17] LOUREDO M., DÍAZ I., GIL J.J., A robotic bone drilling methodology based on position measurements, IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 2012, 1155–1160, Roma, Italy.
  • [18] METZ C.E., Basic principles of ROC analysis, Seminars in Nuclear Medicine, 1978, 8 (4), 283–298.
  • [19] PANDEY R.K., PANDA S.S., Modelling and optimization of temperature in orthopaedic drilling: An in vitro study, Acta of Bioengineering and Biomechanics, 2014, 16 (1), 107–116.
  • [20] PAU H.W., JUST T., BORNITZ M., LASURASHVILLI N., ZAHNERT T., Noise exposure of the inner ear during drilling a cochleostomy for cochlear implantation, The Laryngoscope, 2007, 117, 535–540.
  • [21] PINES J.M., CARPENTER C.R., RAJA A.S., SCHUUR J.D., Evidence-Based Emergency Care: Diagnostic Testing and Clinical Decision Rules, John Wiley & Sons, second edition, 2012.
  • [22] STRESE M., SCHUWERK C., IEPURE A., STEINBACH E., Multimodal featurebased surface material classification, IEEE Transactions on Haptics, 2017, 10 (2), 226–239.
  • [23] WANG Y., DENG Z., SUN Y., YU B., ZHANG P., HU Y., ZHANG J., State detection of bone milling with multi-sensor information fusion, IEEE Conference on Robotics and Biomimetics, 2015, Zhuhai, China.
  • [24] YOUDEN W.J., An index for rating diagnostic tests, Cancer, 1950, 3, 32–35.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b3fc5512-e3d5-4345-bc89-815f6309c118
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