Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review
Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články, přehledy
PubMed
31783631
PubMed Central
PMC6929084
DOI
10.3390/s19235199
PII: s19235199
Knihovny.cz E-zdroje
- Klíčová slova
- CAOS, fluoroscopic navigation, image-based navigation, imageless navigation, medical image processing,
- MeSH
- chirurgie s pomocí počítače trendy MeSH
- lidé MeSH
- muskuloskeletální nemoci patofyziologie chirurgie MeSH
- ortopedické výkony trendy MeSH
- počítačová rentgenová tomografie trendy MeSH
- robotika trendy MeSH
- zobrazování trojrozměrné trendy MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.
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Kowal J., Langlotz F., Nolte L.P. Basics of computer-assisted orthopaedic surgery. Navig. Mis Orthop. Surg. 2007:2–8. doi: 10.1007/978-3-540-36691-1_1. DOI
Sugano N. Computer-assisted orthopaedic surgery and robotic surgery in total hip arthroplasty. Clin. Orthop. Surg. 2013;5:1–9. doi: 10.4055/cios.2013.5.1.1. PubMed DOI PMC
Zheng G., Nolte L.P. Computer-Assisted Orthopedic Surgery: Current State and Future Perspective. Front. Surg. 2015;2:66. doi: 10.3389/fsurg.2015.00066. PubMed DOI PMC
Pei G.-X., Yan Y.-B. Current status and progress of digital orthopaedics in China. J. Orthop. Transl. 2014;2:107–117. doi: 10.1016/j.jot.2014.05.001. DOI
Karhade A.V., Schwab J.H., Bedair H.S. Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty. J. Arthroplast. 2019;34:2272–2277. doi: 10.1016/j.arth.2019.06.013. PubMed DOI
Reina N. Connected orthopedics and trauma surgery: New perspectives. Orthop. Traumatol. Surg. Res. 2019;105:S15–S22. doi: 10.1016/j.otsr.2018.05.018. PubMed DOI
Trauner K.B. The Emerging Role of 3D Printing in Arthroplasty and Orthopedics. J. Arthroplast. 2018;33:2352–2354. doi: 10.1016/j.arth.2018.02.033. PubMed DOI
Ackerman J.D., Keller K., Fuchs H. Real-time anatomical 3D image extraction for laparoscopic surgery. Stud. Health Technol. Inform. 2001;81:18–22. doi: 10.3233/978-1-60750-925-7-18. PubMed DOI
Amiot L.P., Labelle H., Deguise J.A., Sati M., Brodeur P., Rivard C.H. Computer-assisted pedicle screw fixation: A feasibility study. Spine. 1995;20:1208–1212. doi: 10.1097/00007632-199505150-00019. PubMed DOI
Bargar W.L., Bauer A., Börner M. Primary and revision total hip replacement using the ROBODOC® system. Clin. Orthop. Relat. Res. 1998;354:82–91. doi: 10.1097/00003086-199809000-00011. PubMed DOI
Bolger C., Wigfield C. Image-guided surgery: Applications to the cervical and thoracic spine and a review of the first 120 procedures. J. Neurosurg. Spine. 2009;92:172–180. doi: 10.3171/spi.2000.92.2.0175. PubMed DOI
Brown R.A. A computerized tomography-computer graphics approach to stereotaxic localization. J. Neurosurg. 2009;50:715–720. doi: 10.3171/jns.1979.50.6.0715. PubMed DOI
DiGioia A.M., Jaramaz B., Plakseychuk A.Y., Moody J.E., Nikou C., LaBarca R.S., Picard F. Comparison of a mechanical acetabular alignment guide with computer placement of the socket. J. Arthroplast. 2002;17:359–364. doi: 10.1054/arth.2002.30411. PubMed DOI
Hamadeh A., Lavallee S., Cinquin P. Automated 3-dimensional computed tomographic and fluoroscopic image registration. Comput. Aided Surg. 1998;3:11–19. doi: 10.3109/10929089809148123. PubMed DOI
Lavallee S., Troccaz J., Gaborit L., Cinquin P., Benabid A.L., Hoffmann D. Image guided operating robot: A clinical application in stereotactic neurosurgery; Proceedings of the 1992 IEEE International Conference on Robotics and Automation; Washington, DC, USA. 27–30 September 2003; pp. 618–624. DOI
Langlotz F., Nolte L.P. Computer-Assisted Orthopaedic Surgery: From Theory to the Operating Room. Tech. Orthop. 2003;18:140–148. doi: 10.1097/00013611-200306000-00002. DOI
Russakoff D.B., Rohlfing T., Adler J.R., Maurer C.R. Intensity-based 2D-3D spine image registration incorporating a single fiducial marker. Acad. Radiol. 2005;12:287–294. doi: 10.1016/j.acra.2004.09.013. PubMed DOI
Rohlfing T., West J.B., Beier J., Liebig T., Taschner C.A., Thomale U.W. Registration of functional and anatomical MRI: Accuracy assessment and application in navigated neurosurgery. Comput. Aided Surg. 2000;5:414–425. doi: 10.3109/10929080009148901. PubMed DOI
Lim D., Lin F., Wixson R., Hendrix R., MacDonald M., Makhsous M. Accuracy of Imageless Computer Assisted Navigation System through in Total Hip Arthroplasty in vitro and in vivo Studies. World Congr. Med. Phys. Biomed. Eng. 2006:3044–3047. doi: 10.1007/978-3-540-36841-0_771. DOI
Auricchio A., Sorgente A., Soubelet E., Regoli F., Spinucci G., Vaillant R., Moccetti T. Accuracy and usefulness of fusion imaging between three-dimensional coronary sinus and coronary veins computed tomographic images with projection images obtained using fluoroscopy. Europace. 2009;11:1483–1490. doi: 10.1093/europace/eup237. PubMed DOI
Kenngott H.G., Wagner M., Nickel F., Wekerle A.L., Preukschas A., Apitz M., Müller-Stich B.P. Computer-assisted abdominal surgery: New technologies. Langenbeck Arch. Surg. 2015;400:273–281. doi: 10.1007/s00423-015-1289-8. PubMed DOI
Shen D., Wu G., Suk H.I. Deep Learning in Medical Image Analysis. Annu. Rev. Biomed. Eng. 2017;19:221–248. doi: 10.1146/annurev-bioeng-071516-044442. PubMed DOI PMC
Szymkuć S., Gajewska E.P., Klucznik T., Molga K., Dittwald P., Startek M., Grzybowski B.A. Computer-Assisted Synthetic Planning: The End of the Beginning. Angew. Chem. Int. Ed. 2016;55:5904–5937. doi: 10.1002/anie.201506101. PubMed DOI
Bucholz R.D., Laycock K.A. Biomedical Photonics Handbook: Therapeutics and Advanced Biophotonics. 2nd ed. Volume 3. CRC Press; Boca Raton, FL, USA: 2014. Image-guided surgery; pp. 219–238. DOI
Swienckowski J.J., Bono F.S., Spagnuolo M.W. Unicompartmental replacement arthroplasty: A review. Minerva Ortop. E Traumatol. 2005;56:49–63.
Lin H.H., Lo L.J. Three-dimensional computer-assisted surgical simulation and intraoperative navigation in orthognathic surgery: A literature review. J. Formos. Med. Assoc. 2015;114:300–307. doi: 10.1016/j.jfma.2015.01.017. PubMed DOI
Dai Y., Angibaud L., Jung A., Hamad C., Bertrand F., Liu D., Huddleston J. Accuracy of a computer-assisted surgical system for total knee arthroplasy: A review of surgical parameters on 4000+ clinical cases. J. Orthop. Res. 2017;99:20.
Nysjö J. Ph.D. Thesis. Uppsala University; Uppsala, Sweden: 2016. Interactive 3D Image Analysis for Cranio-Maxillofacial Surgery Planning and Orthopedic Applications.
Gérard G. Knee Periprosthetic Infections: CAOS Use in One Stage Procedures. EPiC Ser. Health Sci. 2017;1:391–394. doi: 10.29007/h31s. DOI
Adams L., Krybus W., Meyer-Ebrecht D., Rueger R., Gilsbach J.M., Moesges R., Schloendorff G. Computer-Assisted Surgery. IEEE Comput. Graph. Appl. 1990;10:43–51. doi: 10.1109/38.55152. DOI
Jung R.E., Schneider D., Ganeles J., Wismeijer D., Zwahlen M., Hämmerle C.H.F., Tahmaseb A. Computer technology applications in surgical implant dentistry: A systematic review. Int. J. Oral Maxillofac. Implant. 2009;24:92–109. PubMed
Conole G., Warburton B. A review of computer-assisted assessment. Res. Learn. Technol. 2005;13:17–31. doi: 10.3402/rlt.v13i1.10970. DOI
Contreras Ortiz S.H., Chiu T., Fox M.D. Ultrasound image enhancement: A review. Biomed. Signal Process. Control. 2012;7:419–428. doi: 10.1016/j.bspc.2012.02.002. DOI
Park S.C., Park M.K., Kang M.G. Super-resolution image reconstruction: A technical overview. IEEE Signal Process. Mag. 2003;20:21–36. doi: 10.1109/MSP.2003.1203207. DOI
Bedi S.S., Khandelwal R. Various Image Enhancement Techniques-A Critical Review. International J. Adv. Res. Comput. Commun. Eng. 2013;2:1605–1609.
Subburaj K., Ravi B., Agarwal M.G. Automated 3D geometric reasoning in computer assisted joint reconstructive surgery; Proceedings of the 2009 IEEE International Conference on Automation Science and Engineering; Bangalore, India. 22–25 August 2009; pp. 367–372. DOI
Hernandez D., Garimella R., Eltorai A.E.M., Daniels A.H. Computer-assisted orthopaedic surgery. Orthop. Surg. 2017;9:152–158. doi: 10.1111/os.12323. PubMed DOI PMC
Van der Linden-van der Zwaag H.M.J., Wolterbeek R., Nelissen R.G.H.H. Computer assisted orthopedic surgery; its influence on prosthesis size in total knee replacement. Knee. 2008;15:281–285. doi: 10.1016/j.knee.2008.03.002. PubMed DOI
Jordan A.H., Audia P.G. Self-enhancement and learning from performance feedback. Acad. Manag. Rev. 2012;37:211–231. doi: 10.5465/amr.2010.0108. DOI
Beghdadi A., Larabi M.C., Bouzerdoum A., Iftekharuddin K.M. A survey of perceptual image processing methods. Signal Process. Image Commun. 2013;28:811–831. doi: 10.1016/j.image.2013.06.003. DOI
Shukla K.N., Potnis A., Dwivedy P. A Review on Image Enhancement Techniques. Int. J. Eng. Appl. Comput. Sci. 2017;2:232–235. doi: 10.24032/ijeacs/0207/05. DOI
Eklund A., Dufort P., Forsberg D., LaConte S.M. Medical image processing on the GPU Past, present and future. Med. Image Anal. 2013;17:1073–1094. doi: 10.1016/j.media.2013.05.008. PubMed DOI
Siu W.C., Hung K.W. Review of image interpolation and super-resolution; Proceedings of the 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference; Hollywood, CA, USA. 3–6 December 2012; pp. 1–10.
Aganj I., Yeo B.T.T., Sabuncu M.R., Fischl B. On removing interpolation and resampling artifacts in rigid image registration. IEEE Trans. Image Process. 2013;22:816–827. doi: 10.1109/TIP.2012.2224356. PubMed DOI PMC
Wu Z. A review of statistical methods for preprocessing oligonucleotide microarrays. Stat. Methods Med. Res. 2009;18:533–541. doi: 10.1177/0962280209351924. PubMed DOI PMC
Kumar G., Bhatia P.K. A detailed review of feature extraction in image processing systems; Proceedings of the International Conference on Advanced Computing and Communication Technologies, ACCT; Washington, DC, USA. 8–9 February 2014; pp. 5–12. DOI
Egmont-Petersen M., De Ridder D., Handels H. Image processing with neural networks—A review. Pattern Recognit. 2002;35:2279–2301. doi: 10.1016/S0031-3203(01)00178-9. DOI
Kaur M., Kaur J., Kaur J. Survey of Contrast Enhancement Techniques based on Histogram Equalization. Int. J. Adv. Comput. Sci. Appl. 2013;2:137–141. doi: 10.14569/IJACSA.2011.020721. DOI
Gupta S., Kaur Y. Review of Different Local and Global Contrast Enhancement Techniques for a Digital Image. Int. J. Comput. Appl. 2014;100:18–23. doi: 10.5120/17625-8384. DOI
Singh A., Singh M., Kaur M. Study of Various Image Enhancement Techniques-A Review. Int. J. Comput. Sci. Mob. Comput. 2013;2:186–191.
Kong N.S.P., Ibrahim H., Hoo S.C. A Literature Review on Histogram Equalization and Its Variations for Digital Image Enhancement. Int. J. Innov. Manag. Technol. 2013;4:386–389. doi: 10.7763/IJIMT.2013.V4.426. DOI
Li H., Liu F. Image denoising via sparse and redundant representations over learned dictionaries in wavelet domain; Proceedings of the 5th International Conference on Image and Graphics, ICIG 2009; Xi’an, China. 20–23 September 2009; pp. 754–758. DOI
Balafar M.A., Ramli A.R., Saripan M.I., Mashohor S. Review of brain MRI image segmentation methods. Artif. Intell. Rev. 2010;33:261–274. doi: 10.1007/s10462-010-9155-0. DOI
Smistad E., Falch T.L., Bozorgi M., Elster A.C., Lindseth F. Medical image segmentation on GPUs—A comprehensive review. Med. Image Anal. 2015;20:1–18. doi: 10.1016/j.media.2014.10.012. PubMed DOI
Kaur D., Kaur Y. Various Image Segmentation Techniques: A Review. Int. J. Comput. Sci. Mob. Comput. (IJCSMC) 2014;3:809–814.
Guo Y., Liu Y., Oerlemans A., Lao S., Wu S., Lew M.S. Deep learning for visual understanding: A review. Neurocomputing. 2016;187:27–48. doi: 10.1016/j.neucom.2015.09.116. DOI
Vala H.J., Baxi A. A Review on Otsu Image Segmentation Algorithm. Int. J. Adv. Res. Comput. Eng. Technol. 2013;2:387–389.
Hamuda E., Glavin M., Jones E. A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 2016;125:184–199. doi: 10.1016/j.compag.2016.04.024. DOI
Işin A., Direkoǧlu C., Şah M. Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods. Proc. Comput. Sci. 2016;102:317–324. doi: 10.1016/j.procs.2016.09.407. DOI
Havaei M., Davy A., Warde-Farley D., Biard A., Courville A., Bengio Y., Larochelle H. Brain tumor segmentation with Deep Neural Networks. Med. Image Anal. 2017;35:18–31. doi: 10.1016/j.media.2016.05.004. PubMed DOI
Wajid S.K., Hussain A., Huang K. Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Nov. Feature Extr. Tech. Expert Syst. Appl. 2018;112:388–400. doi: 10.1016/j.eswa.2017.11.057. DOI
Rai H.M., Chatterjee K. Hybrid adaptive algorithm based on wavelet transform and independent component analysis for denoising of MRI images. Meas. J. Int. Meas. Confed. 2019;144:72–82. doi: 10.1016/j.measurement.2019.05.028. DOI
Wu Z., Fu J., Wang Z., Li X., Li J., Pei Y., Fan H. Three-dimensional virtual bone bank system for selecting massive bone allograft in orthopaedic oncology. Int. Orthop. 2015;39:1151–1158. doi: 10.1007/s00264-015-2719-5. PubMed DOI
Fanti Z., Torres F., Arámbula Cosío F. Preliminary results in large bone segmentation from 3D freehand ultrasound. IX Int. Semin. Med. Inf. Process. Anal. 2013;8922:89220F. doi: 10.1117/12.2041809. DOI
Lázár I., Hajdu A. Segmentation of retinal vessels by means of directional response vector similarity and region growing. Comput. Biol. Med. 2015;66:209–221. doi: 10.1016/j.compbiomed.2015.09.008. PubMed DOI
Klintström B., Klintström E., Smedby Ö., Moreno R. Feature space clustering for trabecular bone segmentation. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.) 2017;10270:65–75. doi: 10.1007/978-3-319-59129-2_6. DOI
Bertasius G., Torresani L., Yu S.X., Shi J. Convolutional random walk networks for semantic image segmentation; Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR; Honolulu, HI, USA. 21–26 July 2017; pp. 6137–6145. DOI
Meila M., Shi J. A random walks view of spectral segmentation; Proceedings of the AI and STATISTICS (AISTATS); Key West, FL, USA. 4–7 January 2001; pp. 1–4.
Shamir A. A survey on mesh segmentation techniques. Comput. Graph. Forum. 2008;27:1539–1556. doi: 10.1111/j.1467-8659.2007.01103.x. DOI
Lv J., Chen X., Huangy J., Bao H. Semi-supervised mesh segmentation and labeling. Eur. Symp. Geom. Process. 2012;31:2241–2248. doi: 10.1111/j.1467-8659.2012.03217.x. DOI
Mesejo P., Ibáñez Ó., Cordón Ó., Cagnoni S. A survey on image segmentation using metaheuristic-based deformable models: State of the art and critical analysis. Appl. Sof. Comput. J. 2016;44:1–29. doi: 10.1016/j.asoc.2016.03.004. DOI
Zhang S., Zhan Y., Dewan M., Huang J., Metaxas D.N., Zhou X.S. Deformable segmentation via sparse shape representation. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.) 2011;6892:451–458. doi: 10.1007/978-3-642-23629-7_55. PubMed DOI
Möller M., Lymburner L., Volk M. The comparison index: A tool for assessing the accuracy of image segmentation. Int. J. Appl. Earth Obs. Geoinf. 2007;9:311–321. doi: 10.1016/j.jag.2006.10.002. DOI
Badrinarayanan V., Kendall A., Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2017;39:2481–2495. doi: 10.1109/TPAMI.2016.2644615. PubMed DOI
Sotiras A., Davatzikos C., Paragios N. Deformable medical image registration: A survey. IEEE Trans. Med. Imaging. 2013;32:1153–1190. doi: 10.1109/TMI.2013.2265603. PubMed DOI PMC
Tang L., Hamarneh G. Medical image registration: A review. Med. Imaging Technol. Appl. 2013;1:619–660. doi: 10.1201/b15511. DOI
Alam F., Rahman S.U., Ullah S., Gulati K. Medical image registration in image guided surgery: Issues, challenges and research opportunities. Biocybern. Biomed. Eng. 2018;38:71–89. doi: 10.1016/j.bbe.2017.10.001. DOI
Alam F., Rahman S.U. Intrinsic registration techniques for medical images: A state-of-the-art review. J. Postgrad. Med. Inst. 2016;30:119–132.
Maintz J.B.A., Viergever M.A. An Overview of Medical Image Registration Methods. Volume 12. Utrecht University Repository; Utrecht, The Netherlands: 1996. pp. 1–22.
Alam F., Rahman S.U., Khusro S., Ullah S., Khalil A. Evaluation of medical image registration techniques based on nature and domain of the transformation. J. Med. Imaging Radiat. Sci. 2016;47:178–193. doi: 10.1016/j.jmir.2015.12.081. PubMed DOI
Motai Y., Siddique N.A., Yoshida H. Heterogeneous data analysis: Online learning for medical-image-based diagnosis. Pattern Recognit. 2017;63:612–624. doi: 10.1016/j.patcog.2016.09.035. DOI
Wan R., Li M. An overview of medical image registration; Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2003); Xi’an, China. 20 October 2003; pp. 385–390. DOI
Alam F., Rahman S.U. Challenges and Solutions in Multimodal Medical Image Subregion Detection and Registration. J. Med. Imaging Radiat. Sci. 2019;50:24–30. doi: 10.1016/j.jmir.2018.06.001. PubMed DOI
Alam J., Hassan M., Khan A., Chaudhry A. Robust fuzzy RBF network based image segmentation and intelligent decision making system for carotid artery ultrasound images. Neurocomputing. 2015;151:745–755. doi: 10.1016/j.neucom.2014.10.027. DOI
Markelj P., Tomaževič D., Likar B., Pernuš F. A review of 3D/2D registration methods for image-guided interventions. Med. Image Anal. 2012;16:642–661. doi: 10.1016/j.media.2010.03.005. PubMed DOI
Liu W., Sun J., Li W., Hu T., Wang P. Deep learning on point clouds and its application: A survey. Sensors. 2019;19:4188. doi: 10.3390/s19194188. PubMed DOI PMC
Mani V.R.S., Arivazhagan S. Survey of Medical Image Registration. J. Biomed. Eng. Technol. 2013;1:8–25. doi: 10.12691/JBET-1-2-1. DOI
Kutyniok G., Ma J., März M. Mathematical methods in medical image processing. Quantif. Biophys. Param. Med. Imaging. 2018:153–166. doi: 10.1007/978-3-319-65924-4_7. DOI
Suetens P. Fundamentals of medical imaging. Fundam. Med. Imaging. 2017;6:9375–9389. doi: 10.1017/9781316671849. DOI
Ker J., Wang L., Rao J., Lim T. Deep Learning Applications in Medical Image Analysis. IEEE Access. 2017;6:9375–9389. doi: 10.1109/ACCESS.2017.2788044. DOI
Bouaziz S., Tagliasacchi A., Pauly M. Sparse iterative closest point. Eur. Symp. Geom. Process. 2013;32:113–123. doi: 10.1111/cgf.12178. DOI
Maier-Hein L., Franz A.M., Dos Santos T.R., Schmidt M., Fangerau M., Meinzer H.P., Fitzpatrick J.M. Convergent iterative closest-point algorithm to accomodate anisotropic and inhomogenous localization error. IEEE Trans. Pattern Anal. Mach. Intell. 2012;34:1520–1532. doi: 10.1109/TPAMI.2011.248. PubMed DOI
Serafin J., Grisetti G. NICP: Dense normal based point cloud registration; Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Hamburg, Germany. 28 September–2 October 2015; pp. 742–749. DOI
Marani R., Renò V., Nitti M., D’Orazio T., Stella E. A Modified Iterative Closest Point Algorithm for 3D Point Cloud Registration. Comput.-Aided Civil Infrastruct. Eng. 2016;31:515–534. doi: 10.1111/mice.12184. DOI
Yang J., Li H., Campbell D., Jia Y. Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration. IEEE Trans. Pattern Anal. Mach. Intell. 2016;38:2241–2254. doi: 10.1109/TPAMI.2015.2513405. PubMed DOI
Angibaud L., Silver X., Gulbransen S., Stulberg B. Accuracy of a Novel Computer-Assisted Guidance System for Total Knee Arthroplasty. Bone Jt. J. Orthop. Proc. Suppl. 2013;95:107.
Weil Y., Mosheiff R., Joskowicz L., Liebergall M. Principles of computer-aided surgery in trauma surgery. Navig. MIS Orthop. Surg. 2007:476–485. doi: 10.1007/978-3-540-36691-1_62. DOI
Bae D.K., Song S.J. Computer assisted navigation in knee arthroplasty. Clin. Orthop. Surg. 2011;3:256–267. doi: 10.4055/cios.2011.3.4.259. PubMed DOI PMC
Wong K.C., Kumta S.M. Use of Computer Navigation in Orthopedic Oncology. Curr. Surg. Rep. 2014;2:47. doi: 10.1007/s40137-014-0047-0. PubMed DOI PMC
Fontana E.J., Benzinger T., Cobbs C., Henson J., Fouke S.J. The evolving role of neurological imaging in neuro-oncology. J. Neuro-Oncol. 2014;119:491–502. doi: 10.1007/s11060-014-1505-3. PubMed DOI
Mezger U., Jendrewski C., Bartels M. Navigation in surgery. Langenbeck Arch. Surg. 2013;398:501–514. doi: 10.1007/s00423-013-1059-4. PubMed DOI PMC
Botton-Divet L., Houssaye A., Herrel A., Fabre A.-C., Cornette R. Tools for quantitative formdescription; an evaluation of different software packages for semi-landmark analysis. PeerJ. 2015:1–18. PubMed PMC
Schlicher W., Nielsen I., Huang J.C., Maki K., Hatcher D.C., Miller A.J. Consistency and precision of landmark identification in three-dimensional cone beam computed tomography scans. Eur. J. Orthod. 2012;34:263–275. doi: 10.1093/ejo/cjq144. PubMed DOI
Richards P.J., Kurta I.C., Jasani V., Jones C.H.W., Rahmatalla A., MacKenzie G., Dove J. Assessment of CAOS as a training model in spinal surgery: A randomised study. Eur. Spine J. 2007;16:239–244. doi: 10.1007/s00586-006-0109-9. PubMed DOI PMC
Angibaud L., Dai Y., Jung A., Hamad C., Bertrand F., Huddleston J., Liu D. Geographic variations in the surgical profiles of computer-assisted total knee arthroplasty. J. Orthop. Res. 2017;99:19.
Torres P.M.B., Gonçalves P.J.S., Martins J.M.M. 3D reconstruction and visualization of femur bone structures. Rom. Rev. Precis. Mech. Opt. Mechatron. 2012;41:51–56.
Hafez M.A., DiGioia A.M. Computer-assisted total hip arthroplasty: The present and the future. Future Rheumatol. 2006;1:121–131. doi: 10.2217/17460816.1.1.121. DOI
Akins R., Abdelgawad A.A., Kanlic E.M. Computer Navigation in Orthopedic Trauma: Safer Surgeries with Less Irradiation and More Precision. J. Surg. Orthop. Adv. 2012;21:187–197. doi: 10.3113/JSOA.2012.0187. PubMed DOI
Zaffagnini S., Urrizola F., Signorelli C., Grassi A., Di Sarsina T.R., Lucidi G.A., Muccioli G.M., Bonanzinga T., Marcacci M. Current use of navigation system in ACL surgery: A historical review. Knee Surg. Sport. Traumatol. Arthrosc. 2016;24:3396–3409. doi: 10.1007/s00167-016-4356-y. PubMed DOI
Joskowicz L., Hazan E.J. Computer-aided orthopedic surgery: Incremental shift or paradigm change? Adv. Exp. Med. Biol. 2018;1093:21–30. doi: 10.1007/978-981-13-1396-7_2. PubMed DOI
Mihai S., Filip V. 3D modeling and performing of orthopedic implants by material deposition rapid prototyping. Rom. Rev. Precis. Mech. Opt. Mechatron. 2012;41:128–131.
Stewart C., Akhavan B., Wise S.G., Bilek M.M.M. A review of biomimetic surface functionalization for bone-integrating orthopedic implants: Mechanisms, current approaches, and future directions. Prog. Mater. Sci. 2019;106:100588. doi: 10.1016/j.pmatsci.2019.100588. DOI
Su Y., Cockerill I., Zheng Y., Tang L., Qin Y.-X., Zhu D. Biofunctionalization of metallic implants by calcium phosphate coatings. Bioact. Mater. 2019;4:196–206. doi: 10.1016/j.bioactmat.2019.05.001. PubMed DOI PMC
Mirota D.J., Ishii M., Hager G.D. Vision-Based Navigation in Image-Guided Interventions. Annu. Rev. Biomed. Eng. 2011;13:297–319. doi: 10.1146/annurev-bioeng-071910-124757. PubMed DOI
Eggers G., Mühling J., Marmulla R. Image-to-patient registration techniques in head surgery. Int. J. Oral Maxillofac. Surg. 2006;35:1081–1095. doi: 10.1016/j.ijom.2006.09.015. PubMed DOI
Plooij J.M., Maal T.J.J., Haers P., Borstlap W.A., Kuijpers-Jagtman A.M., Bergé S.J. Digital three-dimensional image fusion processes for planning and evaluating orthodontics and orthognathic surgery. A systematic review. Int. J. Oral. Maxillofac. Surg. 2011;40:341–352. doi: 10.1016/j.ijom.2010.10.013. PubMed DOI
Sattler T., Torii A., Sivic J., Pollefeys M., Taira H., Okutomi M., Pajdla T. Are large-scale 3D models really necessary for accurate visual localization?; Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017; Honolulu, HI, USA. 21–26 July 2017; pp. 6175–6184. DOI
Mirota D.J., Wang H., Taylor R.H., Ishii M., Gallia G.L., Hager G.D. A system for video-Based navigation for endoscopic endonasal skull base surgery. IEEE Trans. Med. Imaging. 2012;31:963–976. doi: 10.1109/TMI.2011.2176500. PubMed DOI
Mason A., Paulsen R., Babuska J.M., Rajpal S., Burneikiene S., Nelson E.L., Villavicencio A.T. The accuracy of pedicle screw placement using intraoperative image guidance systems. J. Neurosurg. Spine. 2013;20:196–203. doi: 10.3171/2013.11.SPINE13413. PubMed DOI
Moses Z.B., Mayer R.R., Strickland B.A., Kretzer R.M., Wolinsky J.P., Gokaslan Z.L., Baaj A.A. Neuronavigation in minimally invasive spine surgery. Neurosurg. Focus. 2013;35:E12. doi: 10.3171/2013.5.FOCUS13150. PubMed DOI
Axel L., Dougherty L. MR imaging of motion with spatial modulation of magnetization. Radiology. 1989;171:841–845. doi: 10.1148/radiology.171.3.2717762. PubMed DOI
Choi S. Decision-Making in Orthopedic and Regional Anesthesiology: A Case-Based Approach. Cambridge University Press; Cambridge, UK: 2015. Total hip arthroplasty; pp. 95–100. DOI
Sperling J.W., Hawkins R.J., Walch G., Zuckerman J.D. Complications in total shoulder arthroplasty. J. Bone Joint Surg. 2013;95:563–569. doi: 10.2106/00004623-201303200-00012. PubMed DOI
Bryan D., Parvizi J., Austin M., Backe H., Valle C., Kolessar D.J., Kreuzer S., Malinzak R., Masri B., McGrory B.J., et al. Obesity and total joint arthroplasty. A literature based review. J. Arthrop. 2013;28:714–721. doi: 10.1016/j.arth.2013.02.011. PubMed DOI
Manrique J., Gomez M.M., Parvizi J. Stiffness after total knee arthroplasty. J. Knee Surg. 2015;28:119–126. doi: 10.1055/s-0034-1396079. PubMed DOI
Helm P.A., Teichman R., Hartmann S.L., Simon D. Spinal Navigation and Imaging: History, Trends, and Future. IEEE Trans. Med. Imaging. 2015;34:1738–1746. doi: 10.1109/TMI.2015.2391200. PubMed DOI
Wang J., Suenaga H., Liao H., Hoshi K., Yang L., Kobayashi E., Sakuma I. Real-time computer-generated integral imaging and 3D image calibration for augmented reality surgical navigation. Comput. Med. Imaging Grap. 2015;40:147–159. doi: 10.1016/j.compmedimag.2014.11.003. PubMed DOI
Yang L., Wang J., Ando T., Kubota A., Yamashita H., Sakuma I., Chiba T., Kobayashi E. Vision-based endoscope tracking for 3D ultrasound image-guided surgical navigation. Comput. Med. Imaging Grap. 2015;40:205–216. doi: 10.1016/j.compmedimag.2014.09.003. PubMed DOI
Yang F., Zhou Z. Recovering 3D planes from a single image via convolutional neural networks. Lect. Notes Comput. Sci. (Incl. Subser. Lectur. Notes Artif. Intell. Lectur. Notes Bioinform.) 2018;11214:84–103. doi: 10.1007/978-3-030-01249-6_6. DOI
Suenaga H., Tran H.H., Liao H., Masamune K., Dohi T., Hoshi K., Takato T. Vision-based markerless registration using stereo vision and an augmented reality surgical navigation system: A pilot study. BMC Med. Imaging. 2015;15:51–62. doi: 10.1186/s12880-015-0089-5. PubMed DOI PMC
Havsteen I., Ohlhues A., Madsen K.H., Nybing J.D., Christensen H., Christensen A. Are movement artifacts in magnetic resonance imaging a real problém?-a narrative review. Front. Neurol. 2017;8:232–240. doi: 10.3389/fneur.2017.00232. PubMed DOI PMC
Ouyang J., Li Q., El Fakhri G. Magnetic resonance-based motion correction for positron emission tomography imaging. Semin. Nucl. Med. 2013;43:60–67. doi: 10.1053/j.semnuclmed.2012.08.007. PubMed DOI PMC
Von Jako R., Finn M.A., Yonemura K.S., Araghi A., Khoo L.T., Carrino J.A., Perez-Cruet M. Minimally invasive percutaneous transpedicular screw fixation: Increased accuracy and reduced radiation exposure by means of a novel electromagnetic navigation system. Acta Neurochir. 2011;153:589–596. doi: 10.1007/s00701-010-0882-4. PubMed DOI PMC
Kawakami Y., Hiranaka T., Matsumoto T., Hida Y., Fukui T., Uemoto H., Doita M., Tsuji M., Kuroda R. The accuracy of bone tunnel position using fluoroscopic-based navigation system in anterior cruciate ligament reconstruction. Knee Surg. Sports Traumatol. Arthrosc. 2012;20:1503–1510. doi: 10.1007/s00167-011-1726-3. PubMed DOI
Weil Y.A., Liebergall M., Mosheiff R., Khoury A. Fluoroscopic Based Navigation in orthopaedic trauma—A review of a large center’s experience. Harefuah. 2018;157:145–148. PubMed
Wang J., Wang Y., Zhu G., Chen X., Zhao X., Qiao H., Fan Y. Influence of the quality of intraoperative fluoroscopic images on the spatial positioning accuracy of a CAOS system. Int. J. Med. Robot. Comput. Assist. Surg. 2018;14:1898. doi: 10.1002/rcs.1898. PubMed DOI
Takao M., Nishii T., Sakai T., Yoshikawa H., Sugano N. Iliosacral screw insertion using CT-3D-fluoroscopy matching navigation. Injury. 2014;45:988–994. doi: 10.1016/j.injury.2014.01.015. PubMed DOI
Uruc V., Ozden R., Dogramaci Y., Kalaci A., Dikmen B., Yildiz O.S., Yengil E. The comparison of freehand fluoroscopic guidance and electromagnetic navigation for distal locking of intramedullary implants. Injury. 2013;44:863–866. doi: 10.1016/j.injury.2012.12.009. PubMed DOI
Mendelsohn D., Strelzow J., Dea N., Ford N.L., Batke J., Pennington A., Street J. Patient and surgeon radiation exposure during spinal instrumentation using intraoperative computed tomography-based navigation. Spine J. 2016;16:343–354. doi: 10.1016/j.spinee.2015.11.020. PubMed DOI
Bourgeois A.C., Faulkner A.R., Bradley Y.C., Pasciak A.S., Barlow P.B., Gash J.R., Reid W.S. Improved accuracy of minimally invasive transpedicular screw placement in the lumbar spine with 3-dimensional stereotactic image guidance: A comparative meta-analysis. J. Spinal Disord. Tech. 2012;8:324–329. doi: 10.1097/BSD.0000000000000152. PubMed DOI
Hahn P., Oezdemir S., Komp M., Giannakopoulos A., Heikenfeld R., Kasch R., Merk H., Godolias G., Ruetten S. A new electromagnetic navigation system for pedicle screws placement: A human cadaver study at the lumbar spine. PLoS ONE. 2015;10:e0133708. doi: 10.1371/journal.pone.0133708. PubMed DOI PMC
Bandela J.R., Jacob R.P., Arreola M., Griglock T.M., Bova F., Yang M. Use of CT-based intraoperative spinal navigation: Management of radiation exposure to operator, staff, and patients. World Neurosurg. 2013;79:390–394. doi: 10.1016/j.wneu.2011.05.019. PubMed DOI
Pandey P., Abugharbieh R., Hodgson A.J. Trackerless 3D Ultrasound Stitching for Computer-Assisted Orthopaedic Surgery and Pelvic Fractures. CAOS. 2017;1:318–321. doi: 10.29007/3wlw. DOI
Varnavas A., Carrell T., Penney G. Increasing the automation of a 2D-3D registration system. IEEE Trans. Med. Imaging. 2013;32:387–399. doi: 10.1109/TMI.2012.2227337. PubMed DOI
Villard J., Ryang Y.M., Demetriades A.K., Reinke A., Behr M., Preuss A., Meyer B., Ringel F. Radiation exposure to the surgeon and the patient during posterior lumbar spinal instrumentation: A prospective randomized comparison of navigated versus non-navigated freehand techniques. Spine. 2014;39:1004–1009. doi: 10.1097/BRS.0000000000000351. PubMed DOI
Slomczykowski M.A., Hofstetter R., Sati M., Krettek C., Nolte L.P. Novel computer-assisted fluoroscopy system for intraoperative guidance: Feasibility study for distal locking of femoral nails. J. Orthop. Trauma. 2001;15:122–131. doi: 10.1097/00005131-200102000-00009. PubMed DOI
Ryang Y.M., Villard J., Obermüller T., Friedrich B., Wolf P., Gempt J., Meyer B. Learning curve of 3D fluoroscopy image-guided pedicle screw placement in the thoracolumbar spine. Spine J. 2015;15:467–476. doi: 10.1016/j.spinee.2014.10.003. PubMed DOI
Wassilew G.I., Perka C., Janz V., König C., Asbach P., Hasart O. Use of an Ultrasound-Based Navigation System for an Accurate Acetabular Positioning in Total Hip Arthroplasty. A Prospective, Randomized, Controlled Study. J. Arthrop. 2012;27:687–694. doi: 10.1016/j.arth.2011.06.038. PubMed DOI
Turley G.A., Ahmed S.M.Y., Williams M.A., Griffin D.R. Validation of the femoral anteversion measurement method used in imageless navigation. Comput. Aided Surg. 2012;17:187–197. doi: 10.3109/10929088.2012.690230. PubMed DOI PMC
Audenaert E., Smet B., Pattyn C., Khanduja V. Imageless versus image-based registration in navigated arthroscopy of the hip. J. Bone Joint Surg. 2012;94:624–629. doi: 10.1302/0301-620X.94B5.28627. PubMed DOI
Liu Z., Gao Y., Cai L. Imageless navigation versus traditional method in total hip arthroplasty: A meta-analysis. Int. J. Surg. 2015;21:122–127. doi: 10.1016/j.ijsu.2015.07.707. PubMed DOI
Scholes C., Sahni V., Lustig S., Parker D.A., Coolican M.R.J. Patient-specific instrumentation for total knee arthroplasty does not match the pre-operative plan as assessed by intra-operative computer-assisted navigation. Knee Surg. Sport. Traumatol. Arthrosc. 2014;22:660–665. doi: 10.1007/s00167-013-2670-1. PubMed DOI
Weber M., Woerner M., Messmer B., Grifka J., Renkawitz T. Navigation is Equal to Estimation by Eye and Palpation in Preventing Psoas Impingement in THA. Clin. Orthop. Relat. Res. 2017;475:196–203. doi: 10.1007/s11999-016-5061-3. PubMed DOI PMC
Deep K., Shankar S., Mahendra A. Computer assisted navigation in total knee and hip arthroplasty. SICOT-J. 2017;3:50–56. doi: 10.1051/sicotj/2017034. PubMed DOI PMC
Härtl R., Lam K.S., Wang J., Korge A., Kandziora F., Audigé L. Worldwide survey on the use of navigation in spine surgery. World Neurosurg. 2013;79:162–172. doi: 10.1016/j.wneu.2012.03.011. PubMed DOI
Wasterlain A.S., Buza J.A., Thakkar S.C., Schwarzkopf R., Vigdorchik J. Navigation and robotics in total hip arthroplasty. JBJS Rev. 2017;5:2. doi: 10.2106/JBJS.RVW.16.00046. PubMed DOI
Nam D., Maher P.A., Rebolledo B.J., Nawabi D.H., McLawhorn A.S., Pearle A.D. Patient specific cutting guides versus an imageless, computer-assisted surgery system in total knee arthroplasty. Knee. 2013;20:263–267. doi: 10.1016/j.knee.2012.12.009. PubMed DOI
Chen T.K., Abolmaesumi P., Pichora D.R., Ellis R.E. A system for ultrasound-guided computer-assisted orthopaedic surgery. Comput. Aided Surg. 2005;10:281–292. doi: 10.1080/10929080500390017. PubMed DOI
Gonçalves P.J.S., Torres P.M.B., Santos F., António R., Catarino N., Martins J.M.M. A Vision System for Robotic Ultrasound Guided Orthopaedic Surgery. J. Int. Robot. Syst. Theory Appl. 2014;77:327–339. doi: 10.1007/s10846-013-0012-7. DOI
Atesok K., Schemitsch E. Computer-assisted Trauma surgery. J. Am. Acad. Orthop. Surg. 2010;18:247–258. doi: 10.5435/00124635-201005000-00001. PubMed DOI
Mozes A., Chang T.C., Arata L., Zhao W. Three-dimensional A-mode ultrasound calibration and registration for robotic orthopaedic knee surgery. Int. J. Med. Robot. Comput. Assist. Surg. 2010;6:91–101. doi: 10.1002/rcs.294. PubMed DOI
Vercruyssen M., Hultin M., Van Assche N., Svensson K., Naert I., Quirynen M. Guided surgery: Accuracy and efficacy. Periodontology. 2000;66:228–246. doi: 10.1111/prd.12046. PubMed DOI
Ohashi H., Matsuura M., Okamoto Y., Okajima Y. Intra- and intersurgeon variability in image-free navigation system for THA. Clin. Orthop. Relat. Res. 2009;467:2305–2309. doi: 10.1007/s11999-009-0833-7. PubMed DOI PMC
Ryan J.A., Jamali A.A., Bargar W.L. Accuracy of computer navigation for acetabular component placement in. Clin. Orthop. Relat. Res. 2010;468:169–177. doi: 10.1007/s11999-009-1003-7. PubMed DOI PMC
Lin F., Lim D., Wixson R.L., Milos S., Hendrix R.W., Makhsous M. Limitations of Imageless Computer-Assisted Navigation for Total Hip Arthroplasty. J. Arthrop. 2011;26:596–605. doi: 10.1016/j.arth.2010.05.027. PubMed DOI
Hohmann E., Bryant A., Tetsworth K. Anterior Pelvic Soft Tissue Thickness Influences Acetabular Cup Positioning with Imageless Navigation. J. Arthrop. 2012;27:945–952. doi: 10.1016/j.arth.2011.09.017. PubMed DOI
Stiehl J.B., Heck D.A., Jaramaz B., Amiot L.-P. Comparison of fluoroscopic and imageless registration in surgical navigation of the acetabular component. Comput. Aided Surg. 2007;12:116–124. doi: 10.3109/10929080701292939. PubMed DOI
Cobb J.P., Kannan V., Dandachli W., Iranpour F., Brust K.U., Hart A.J. Learning how to resurface cam-type femoral heads with acceptable accuracy and precision: The role of computed tomography-based navigation. J. Bone Joint Surg. 2008;90:57–64. doi: 10.2106/JBJS.H.00606. PubMed DOI
Pitto R.P., Malak S., Anderson I.A. Accuracy of computer-assisted navigation for femoral head resurfacing decreases in hips with abnormal anatomy. Clin. Orthop. Relat. Res. 2009;467:2310–2317. doi: 10.1007/s11999-009-0850-6. PubMed DOI PMC
Subramanian P., Wainwright T.W., Bahadori S., Middleton R.G. A review of the evolution of robotic-assisted total hip arthroplasty. HIP Int. 2019;29:232–238. doi: 10.1177/1120700019828286. PubMed DOI
Jia Z., Du Z., Wang M. A novel finite element method based biomechanical model for HIT-robot assisted orthopedic surgery system; Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology; New York, NY, USA. 31 August–3 September 2006; pp. 6505–6508. PubMed
Wang M. Development and validity of tissue biomechanics modeling for virtual robot assisted orthopedic surgery system; Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009; Beijing, China. 11–16 June 2009.
Bai L., Yang J., Chen X., Sun Y., Li X. Medical robotics in bone fracture reduction surgery: A review. Sensors. 2019;19:3593. doi: 10.3390/s19163593. PubMed DOI PMC
Zhao J.-X., Li C., Ren H., Hao M., Zhang L.-C., Tang P.-F. Evolution and Current Applications of Robot-Assisted Fracture Reduction: A Comprehensive Review. Ann. Biomed. Eng. 2019 doi: 10.1007/s10439-019-02332-y. PubMed DOI
Giannoudis P.V., Tzioupis C.C., Pape H.C., Roberts C.S. Percutaneous fixation of the pelvic ring: An update. J. Bone Jt. Surg. Br. Vol. 2007;89:145–154. doi: 10.1302/0301-620X.89B2.18551. PubMed DOI
Saragaglia D., Rubens-Duval B., Gaillot J., Lateur G., Pailhé R. Total knee arthroplasties from the origin to navigation: history, rationale, indications. Int. Orthopaedics. 2019;43:597–604. doi: 10.1007/s00264-018-3913-z. PubMed DOI
Abdelgawad A., Akins R., Kanlic E.M. Use of computer assisted orthopedic surgery in pelvic and acetabular trauma. Acta Med. Acad. 2011;40:166–173. doi: 10.5644/ama2006-124.20. DOI
Jabran A., Peach C., Ren L. Biomechanical analysis of plate systems for proximal humerus fractures: A systematic literature review. Biomed. Eng. Online. 2018;17:47–77. doi: 10.1186/s12938-018-0479-3. PubMed DOI PMC
Jabran A., Peach C., Zou Z., Ren L. Biomechanical comparison of screw-based zoning of PHILOS and Fx proximal humerus plates. BMC Musculoskelet. Disord. 2018;19:253–263. doi: 10.1186/s12891-018-2185-5. PubMed DOI PMC
Dirhold B.M., Citak M., Al-Khateeb H., Haasper C., Kendoff D., Krettek C., Citak M. Current state of computer-assisted trauma surgery. Curr. Rev. Musculoskelet. Med. 2012;5:184–191. doi: 10.1007/s12178-012-9133-z. PubMed DOI PMC
Kanlić E.M., DeLaRosa F., Pirela-Cruz M. Computer assisted orthopaedic surgery—CAOS. Bosn. J. Basic Med. Sci. 2006;6:7–14. doi: 10.17305/bjbms.2006.3202. PubMed DOI PMC
Porcellini G., Tarallo L., Novi M., Spiezia F., Catani F. Technology applications in shoulder replacement. J. Orthop. Traumatol. 2019;20:27. doi: 10.1186/s10195-019-0535-1. PubMed DOI PMC
Biazzo A., Confalonieri N. Computer-assisted surgery in total knee replacement: Advantages, surgical procedure and review of the literature. Acta Biomed. 2019;90:16–23. PubMed PMC
Weil Y.A., Liebergall M., Khoury A. Computer assisted surgery for iliosacral screw placement-how far have we gone? J. Trauma Treat. 2016;5:345–352. doi: 10.4172/2167-1222.1000345. DOI
Jabran A., Peach C., Zou Z., Ren L. Parametric Design Optimisation of Proximal Humerus Plates Based on Finite Element Method. Ann. Biomed. Eng. 2019;47:601–614. doi: 10.1007/s10439-018-02160-6. PubMed DOI PMC
Apostolov P., Burnev M., Milkov P. Methods and techniques of percutaneous external fixation in pelvic fractures. J. IMAB Annu. Proc. 2011;17:166–171. doi: 10.5272/jimab.2011171.166. DOI
Jabran A., Peach C., Zou Z., Ren L. Hybrid blade and locking plate fixation for proximal humerus fractures: A comparative biomechanical analysis. Biomed. Eng. Online. 2018;17:10. doi: 10.1186/s12938-018-0447-y. PubMed DOI PMC
Worth A.J., Crosse K.R., Kersley A. Computer-Assisted Surgery Using 3D Printed Saw Guides for Acute Correction of Antebrachial Angular Limb Deformities in Dogs. Vet. Comp. Orthop. Traumatol. 2019;32:241–249. doi: 10.1055/s-0039-1678701. PubMed DOI
United Nations Office of Legal Affairs . Treaty Series: Treaties and International Agreements Registered or Filed and Recorded with the Secretariat of the United Nations. United Nations; New York, NY, USA: 1986.
Fang C., Cai H., Kuong E., Chui E., Siu Y.C., Ji T., Drstvenšek I. Surgical applications of three-dimensional printing in the pelvis and acetabulum: From models and tools to implants. Unfallchirurg. 2019;122:278–285. doi: 10.1007/s00113-019-0626-8. PubMed DOI PMC
Börm W., König R.W., Albrecht A., Richter H.P., Kast E. Percutaneous transarticular atlantoaxial screw fixation using a cannulated screw system and image guidance. Minim. Invasive Neurosurg. 2004;47:111–114. doi: 10.1055/s-2004-818449. PubMed DOI
Amoretti N., Marcy P.Y., Hauger O., Browaeys P., Amoretti M.E., Hoxorka I., Boileau P. Percutaneous screw fixation of a vertebral pedicle fracture under CT-guidance: A new technique. Eur. J. radiol. 2012;81:591–593. doi: 10.1016/j.ejrad.2011.01.058. PubMed DOI
Iorio J.A., Jakoi A.M., Rehman S. Percutaneous Sacroiliac Screw Fixation of the Posterior Pelvic Ring. Orthop. Clin. North Am. 2015;46:511–521. doi: 10.1016/j.ocl.2015.06.005. PubMed DOI
Klassen P.D., Baume B., Elsharkawy A.E. Percutaneous posterior combined C2 translaminar and pedicle screws using Intraoperative O-arm Navigation in an atypical traumatic spondylolisthesis: Technical notes. Interdiscip. Neurosurg. 2017;9:39–40. doi: 10.1016/j.inat.2017.03.001. DOI
Askari M., Shin A.Y. Extraction of cannulated percutaneous screw from scaphoid: A simplified technique. J. Hand Surg. 2012;37:1702–1705. doi: 10.1016/j.jhsa.2012.05.027. PubMed DOI
Biber R., Pauser J., Brem M., Bail H.J. Bioabsorbable metal screws in traumatology: A promising innovation. Trauma Case Rep. 2017;8:11–15. doi: 10.1016/j.tcr.2017.01.012. PubMed DOI PMC
Chew F.S. Musculoskeletal Imaging: The Essentials. Lippincott Williams and Wilkins; Philadelphia, PA, USA: 2018.
Acar B., Kose O., Kati Y.A., Egerci O.F., Turan A., Yuksel H.Y. Comparison of volar versus dorsal screw fixation for scaphoid waist fractures: A finite element analysis. Orthop. Traumatol. Surg. Res. 2018;104:1107–1113. doi: 10.1016/j.otsr.2018.07.013. PubMed DOI
Le L., Jabran A., Peach C., Ren L. Effect of screw thread length on stiffness of proximal humerus locking plate constructs: A finite element study. Med. Eng. Phys. 2019;63:79–87. doi: 10.1016/j.medengphy.2018.12.004. PubMed DOI
Vigdorchik J.M., Jin X., Sethi A., Herzog D.T., Oliphant B.W., Yang K.H., Vaidya R. A biomechanical study of standard posterior pelvic ring fixation versus a posterior pedicle screw construct. Injury. 2015;46:1491–1496. doi: 10.1016/j.injury.2015.04.038. PubMed DOI
Zheng X., Chaudhari R., Wu C., Mehbod A.A., Erkan S., Transfeldt E.E. Biomechanical evaluation of an expandable meshed bag augmented with pedicle or facet screws for percutaneous lumbar interbody fusion. Spine J. 2010;10:987–993. doi: 10.1016/j.spinee.2010.08.016. PubMed DOI
Palumbo B.T., Nalley C., Gaskins R.B., III, Gutierrez S., Alexander G.E., III, Anijar L., Santoni B.G. Biomechanical analysis of impending femoral neck fractures: The role of percutaneous cement augmentation for osteolytic lesions. Clin. Biomech. 2014;29:289–295. doi: 10.1016/j.clinbiomech.2013.12.001. PubMed DOI
Ropars M., Mitton D., Skalli W. Minimally invasive screw plates for surgery of unstable intertrochanteric femoral fractures: A biomechanical comparative study. Clin. Biomech. 2008;23:1012–1017. doi: 10.1016/j.clinbiomech.2008.04.018. PubMed DOI
Park Y., Ha J.W., Lee Y.T., Sung N.Y. Percutaneous placement of pedicle screws in overweight and obese patients. Spine J. 2011;11:919–924. doi: 10.1016/j.spinee.2011.07.029. PubMed DOI
Weninger P., Dall’Ara E., Leixnering M., Pezzei C., Hertz H., Drobetz H., Zysset P. Volar fixed-angle plating of extra-articular distal radius fractures—A biomechanical analysis comparing threaded screws and smooth pegs. J. Trauma Acute Care Surg. 2010;69:E46–E55. doi: 10.1097/TA.0b013e3181c6630e. PubMed DOI
Yao J., Park M.J., Patel C.S. Biomechanical comparison of volar locked plate constructs using smooth and threaded locking pegs. Orthopedic. 2014;37:E169–E173. doi: 10.3928/01477447-20140124-21. PubMed DOI
Chudik S.C., Weinhold P., Dahners L.E. Fixed-angle plate fixation in simulated fractures of the proximal humerus: A biomechanical study of a new device. J. Shoulder Elb. Surg. 2003;12:578–588. doi: 10.1016/S1058-2746(03)00217-9. PubMed DOI
Siffri P.C., Peindl R.D., Coley E.R., Norton J., Connor P.M., Kellam J.F. Biomechanical analysis of blade plate versus locking plate fixation for a proximal humerus fracture: Comparison using cadaveric and synthetic humeri. J. Orthop. Trauma. 2006;20:547–554. doi: 10.1097/01.bot.0000244997.52751.58. PubMed DOI
La Rosa G., Clienti C., Mineo R., Audenino A. Experimental analysis of pedicle screws. Proc. Struct. Integr. 2016;2:1244–1251. doi: 10.1016/j.prostr.2016.06.159. DOI
Clin J., Le Navéaux F., Driscoll M., Mac-Thiong J.M., Labelle H., Parent S., Serhan H. Biomechanical Comparison of the Load-Sharing Capacity of High and Low Implant Density Constructs with Three Types of Pedicle Screws for the Instrumentation of Adolescent Idiopathic Scoliosis. Spine Deform. 2019;7:2–10. doi: 10.1016/j.jspd.2018.06.007. PubMed DOI
Lonstein J.E., Denis F., Perra J.H., Pinto M.R., Smith M.D., Winter R.B. Complications associated with pedicle screws. JBJS. 1999;81:1519–1528. doi: 10.2106/00004623-199911000-00003. PubMed DOI
Carreau J.H., Bastrom T., Petcharaporn M., Schulte C., Marks M., Illés T., Newton P.O. Computer-generated, three-dimensional spine model from biplanar radiographs: A validity study in idiopathic scoliosis curves greater than 50 degrees. Spine Deform. 2014;2:81–88. doi: 10.1016/j.jspd.2013.10.003. PubMed DOI
Humbert L., De Guise J.A., Aubert B., Godbout B., Skalli W. 3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences. Med. Eng. Phys. 2009;31:681–687. doi: 10.1016/j.medengphy.2009.01.003. PubMed DOI
Shirazi-Adl S.A., Shrivastava S.C., Ahmed A.M. Stress analysis of the lumbar disc-body unit in compression. A three-dimensional nonlinear finite element study. Spine. 1984;9:120–134. doi: 10.1097/00007632-198403000-00003. PubMed DOI
Bharucha N.J., Lonner B.S., Auerbach J.D., Kean K.E., Trobisch P.D. Low-density versus high-density thoracic pedicle screw constructs in adolescent idiopathic scoliosis: Do more screws lead to a better outcome? Spine J. 2013;13:375–381. doi: 10.1016/j.spinee.2012.05.029. PubMed DOI
Larson A.N., Polly D.W., Jr., Diamond B., Ledonio C., Richards B.S., III, Emans J.B., Sucato D.J., Johnston C.E. Minimize Implants Maximize Outcomes Study Group. Does higher anchor density result in increased curve correction and improved clinical outcomes in adolescent idiopathic scoliosis? Spine. 2014;39:571–578. doi: 10.1097/BRS.0000000000000204. PubMed DOI
Gotfryd A.O., Avanzi O. Randomized clinical study on surgical techniques with different pedicle screw densities in the treatment of adolescent idiopathic scoliosis types Lenke 1A and 1B. Spine Deform. 2013;1:272–279. doi: 10.1016/j.jspd.2013.05.004. PubMed DOI
Larson A.N., Aubin C.E., Polly D.W., Jr., Ledonio C.G., Lonner B.S., Shah S.A., Richards B.S., III, Erickson M.A., Emans J.B., Weinstein S.L., et al. Are more screws better? A systematic review of anchor density and curve correction in adolescent idiopathic scoliosis. Spine Deform. 2013;1:237–247. doi: 10.1016/j.jspd.2013.05.009. PubMed DOI
Kubiak A.J., Lindqvist-Jones K., Dearn K.D., Shepherd D.E. Comparison of the mechanical properties of two designs of polyaxial pedicle screw. Eng. Fail. Anal. 2019;95:96–106. doi: 10.1016/j.engfailanal.2018.08.023. DOI
Arslan A.K., Demir T., Örmeci M.F., Camuşcu N., Türeyen K. Postfusion pullout strength comparison of a novel pedicle screw with classical pedicle screws on synthetic foams. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 2013;227:114–119. doi: 10.1177/0954411912463323. PubMed DOI
Gelgor I.E., Karaman A.I., Buyukyilmaz T. Comparison of 2 distalization systems supported by intraosseous screws. Am. J. Orthod. Dentofac. Orthop. 2007;131:161E1–161E8. doi: 10.1016/j.ajodo.2006.03.027. PubMed DOI
Amasyalı M., Sabuncuoğlu F.A., Oflaz U. Intraoral Molar Distalization with Intraosseous Mini Screw. Turk. J. Orthod. 2018;31:26–30. doi: 10.5152/TurkJOrthod.2018.17030. PubMed DOI PMC
Cullen P.M. Intraosseous cannulation in children. Anaesth. Intensiv. Care Med. 2014;15:567–569. doi: 10.1016/j.mpaic.2014.09.006. DOI
Rony L., Lancigu R., Hubert L. Intraosseous metal implants in orthopedics: A review. Morphologie. 2018;102:231–242. doi: 10.1016/j.morpho.2018.09.003. PubMed DOI
Xiao M., Chen Y.M., Biao M.N., Zhang X.D., Yang B.C. Bio-functionalization of biomedical metals. Mater. Sci. Eng. C. 2017;70:1057–1070. doi: 10.1016/j.msec.2016.06.067. PubMed DOI
Sampatacos N., Getelman M.H., Henninger H.B. Biomechanical comparison of two techniques for arthroscopic suprapectoral biceps tenodesis: Interference screw versus implant-free intraosseous tendon fixation. J. Shoulder Elb. Surg. 2014;23:1731–1739. doi: 10.1016/j.jse.2014.02.027. PubMed DOI
Hordyk P.J., Fuerbringer B.A., Roukis T.S. Clinical Management of Acute, Closed Displaced Intra-Articular Calcaneal Fractures. Clin. Podiatry Med. Surg. 2019;36:163–171. doi: 10.1016/j.cpm.2018.10.001. PubMed DOI
Scott R.T., Hyer C.F., DeMill S.L. Screw fixation diameter for fifth metatarsal Jones fracture: A cadaveric study. J. Foot Ankl. Surg. 2015;54:227–229. doi: 10.1053/j.jfas.2014.11.010. PubMed DOI
Roukis T.S. Closed Manipulation, Intraosseous Reduction, and Rigid Internal Fixation for Displaced Intra-Articular Calcaneal Fractures. Clin. Podiatry Med. Surg. 2019;36:197–210. doi: 10.1016/j.cpm.2018.10.003. PubMed DOI
Javaid M., Haleem A. Additive manufacturing applications in orthopaedics: A review. J. Clin. Orthop. Trauma. 2018;9:202–206. doi: 10.1016/j.jcot.2018.04.008. PubMed DOI PMC
Solomin L., editor. The Basic Principles of External Skeletal Fixation Using the Ilizarov and Other Devices. Springer Science & Business Media; Berlin/Heidelberg, Germany: 2013.
Garg S., Quick H.D., Kim E.B., Erickson M.A. Use of Activity Trackers in Orthopaedics. J. Am. Acad. Orthop. Surg. 2019;27:e859–e866. doi: 10.5435/JAAOS-D-18-00546. PubMed DOI
Giordano V., Godoy-Santos A.L., Belangero W.D., Pires R.E.S., Labronici P.J., Koch H.A. Finite element analysis of the equivalent stress distribution in Schanz screws during the use of a femoral fracture distractor. Revista Brasileira de Ortopedia. 2017;52:396–401. doi: 10.1016/j.rbo.2016.06.009. PubMed DOI PMC
Sonohata M., Kitajima M., Kawano S., Tanaka R., Mawatari M. Total hip arthroplasty with femoral subtrochanteric osteotomy after Schanz osteotomy. J. Orthop. Sci. 2016;21:469–474. doi: 10.1016/j.jos.2016.02.012. PubMed DOI
Frydrýšek K., Jořenek J., Učeň O., Kub’n T., Žilka L., Pleva L. Design of external fixators used in traumatology and orthopaedics–treatment of fractures of pelvis and its acetabulum. Procedia Eng. 2012;48:164–173. doi: 10.1016/j.proeng.2012.09.501. DOI
Evans M., Spencer M., Wang Q., White S.H., Cunningham J.L. Design and testing of external fixator bone screws. J. Biomed. Eng. 1990;12:457–462. doi: 10.1016/0141-5425(90)90054-Q. PubMed DOI
Tomanec F., Rusnáková S., Kalová M., Maňas L. Innovation of ilizarov stabilization device with the design changes. MM Sci. J. 2019;3:2732–2738. doi: 10.17973/MMSJ.2019_03_2018005. DOI
Qiao F., Li D., Jin Z., Gao Y., Zhou T., He J., Cheng L. Application of 3D printed customized external fixator in fracture reduction. Injury. 2015;46:1150–1155. doi: 10.1016/j.injury.2015.01.020. PubMed DOI
Heidari B.S., Oliaei E., Shayesteh H., Davachi S.M., Hejazi I., Seyfi J., Bahrami M., Rashedi H. Simulation of mechanical behavior and optimization of simulated injection molding process for PLA based antibacterial composite and nanocomposite bone screws using central composite design. J. Mech. Behav. Biomed. Mater. 2017;65:160–176. doi: 10.1016/j.jmbbm.2016.08.008. PubMed DOI
Heidari B.S., Davachi S.M., Moghaddam A.H., Seyfi J., Hejazi I., Sahraeian R., Rashedi H. Optimization simulated injection molding process for ultrahigh molecular weight polyethylene nanocomposite hip liner using response surface methodology and simulation of mechanical behavior. J. Mech. Behav. Biomed. Mater. 2018;81:95–105. doi: 10.1016/j.jmbbm.2018.02.025. PubMed DOI
Kazemian G.H., Manafi A.R., Najafi F., Najafi M.A. Treatment of intertrochanteric fractures in elderly highrisk patients: Dynamic hip screw vs. external fixation. Injury. 2014;45:568–572. doi: 10.1016/j.injury.2013.11.020. PubMed DOI
Carpenter B., Bohay D., Early J.S., Jennings M., Pomeroy G., Schuberth J.M., Wukich D.K. Cannulated Screws. J. Foot Ankle Surg. 2019;58:333–336. doi: 10.1053/j.jfas.2018.08.035. PubMed DOI
Capuder K., Gill C., Hafez J., Kawalec J., Hetherington V. Effect of repeated cycles of steam sterilization on the integrity of cannulated surgical screws. Foot. 2019;39:88–91. doi: 10.1016/j.foot.2019.02.010. PubMed DOI
Kemker B., Magone K., Owen J., Atkinson P., Martin S., Atkinson T. A sliding hip screw augmented with 2 screws is biomechanically similar to an inverted triad of cannulated screws in repair of a Pauwels type-III fracture. Injury. 2017;48:1743–1748. doi: 10.1016/j.injury.2017.05.013. PubMed DOI
Mei J., Liu S., Jia G., Cui X., Jiang C., Ou Y. Finite element analysis of the effect of cannulated screw placement and drilling frequency on femoral neck fracture fixation. Injury. 2014;45:2045–2050. doi: 10.1016/j.injury.2014.07.014. PubMed DOI
Panteli M., Rodham P., Giannoudis P.V. Biomechanical rationale for implant choices in femoral neck fracture fixation in the non-elderly. Injury. 2015;46:445–452. doi: 10.1016/j.injury.2014.12.031. PubMed DOI
Tolunay T., Arslan K., Yaman O., Dalbayrak S., Demir T. Biomechanical performance of various cement-augmented cannulated pedicle screw designs for osteoporotic bones. Spine Deform. 2015;3:205–210. doi: 10.1016/j.jspd.2014.09.055. PubMed DOI
Chen L.H., Tai C.L., Lai P.L., Lee D.M., Tsai T.T., Fu T.S., Niu C.C., Chen W.J. Pullout strength for cannulated pedicle screws with bone cement augmentation in severely osteoporotic bone: Influences of radial hole and pilot hole tapping. Clin. Biomech. 2009;24:613–618. doi: 10.1016/j.clinbiomech.2009.05.002. PubMed DOI
Shih K.S., Hsu C.C., Hou S.M., Yu S.C., Liaw C.K. Comparison of the bending performance of solid and cannulated spinal pedicle screws using finite element analyses and biomechanical tests. Med. Eng. Phys. 2015;37:879–884. doi: 10.1016/j.medengphy.2015.06.008. PubMed DOI
Gruszka D., Nowak T.E., Tkacz T., Wagner D., Rommens P.M. Complex radial head and neck fractures treated with modern locking plate fixation. J. Shoulder Elb. Surg. 2019;28:1130–1138. doi: 10.1016/j.jse.2018.11.056. PubMed DOI
Morton D., Phasuk K., Polido W.D., Lin W.S. Consideration for Contemporary Implant Surgery. Dent. Clin. 2019;63:309–329. doi: 10.1016/j.cden.2018.11.010. PubMed DOI
Jabran A., Ren L., Peach C., Zou Z. A Methodology for Biomechanical Assessment of Proximal Humerus Fractures Using an Integrated Experimental and Computational Framework. Proc. CIRP. 2016;49:139–142. doi: 10.1016/j.procir.2015.11.003. DOI
LaMartina J., II, Christmas K.N., Simon P., Streit J.J., Allert J.W., Clark J., Frankle M.A. Difficulty in decision making in the treatment of displaced proximal humerus fractures: The effect of uncertainty on surgical outcomes. J. Shoulder Elb. Surg. 2018;27:470–477. doi: 10.1016/j.jse.2017.09.033. PubMed DOI
Varga P., Inzana J.A., Gueorguiev B., Südkamp N.P., Windolf M. Validated computational framework for efficient systematic evaluation of osteoporotic fracture fixation in the proximal humerus. Med. Eng. Phys. 2018;57:29–39. doi: 10.1016/j.medengphy.2018.04.011. PubMed DOI
Bai L., Gong C., Chen X., Sun Y., Zhang J., Cai L., Zhu S., Xie S.Q. Additive manufacturing of customized metallic orthopedic implants: Materials, structures, and surface modifications. Metals. 2019;9:1004. doi: 10.3390/met9091004. DOI
Li J.-W., Du C.-F., Yuchi C.-X., Zhang C.-Q. Application of Biodegradable Materials in Orthopedics. J. Med Biol. Eng. 2019;39:633–645. doi: 10.1007/s40846-019-00469-8. DOI
Shafaghi R., Rodriguez O., Schemitsch E.H., Zalzal P., Waldman S.D., Papini M., Towler M.R. A review of materials for managing bone loss in revision total knee arthroplasty. Mater. Sci. Eng. C. 2019;104:109941. doi: 10.1016/j.msec.2019.109941. PubMed DOI
Kaur M., Singh K. Review on titanium and titanium based alloys as biomaterials for orthopaedic applications. Mater. Sci. Eng. C. 2019;102:844–862. PubMed
Hu C., Ashok D., Nisbet D.R., Gautam V. Bioinspired surface modification of orthopedic implants for bone tissue engineering. Biomaterial. 2019;219:119366. doi: 10.1016/j.biomaterials.2019.119366. PubMed DOI
Zhao J., Liu Y., Fan M., Liu B., He D., Tian W. Comparison of the clinical accuracy between point-to-point registration and auto-registration using an active infrared navigation system. Spine. 2018;43:E1329–E1333. doi: 10.1097/BRS.0000000000002704. PubMed DOI
Wein W., Karamalis A., Baumgartner A., Navab N. Automatic bone detection and soft tissue aware ultrasound–CT registration for computer-aided orthopedic surgery. Int. J. Comput. Assist. Radiol. Surg. 2015;10:971–979. doi: 10.1007/s11548-015-1208-z. PubMed DOI
Du H., Hu L., Li C., Wang T., Zhao L., Li Y., Mao Z., Liu D., Zhang L., He C., et al. Advancing computer-assisted orthopaedic surgery using a hexapod device for closed diaphyseal fracture reduction. Int. J. Med. Robot. Comput. Assist. Surg. 2015;11:348–359. doi: 10.1002/rcs.1614. PubMed DOI
Clarke J.V., Deakin A.H., Nicol A.C., Picard F. Measuring the positional accuracy of computer assisted surgical tracking systems. Comput. Aided Surg. 2010;15:13–18. doi: 10.3109/10929081003775774. PubMed DOI
Gao L., Madry H., Chugaev D.V., Denti M., Frolov A., Burtsev M., Magnitskaya N., Mukhanov V., Neyret P., Solomin L.N., et al. Advances in modern osteotomies around the knee: Report on the Association of Sports Traumatology, Arthroscopy, Orthopaedic surgery, Rehabilitation (ASTAOR) Moscow International Osteotomy Congress 2017. J. Exp. Orthop. 2019;6:9. doi: 10.1186/s40634-019-0177-5. PubMed DOI PMC
Picard F., Deakin A.H., Riches P.E., Deep K., Baines J. Computer assisted orthopaedic surgery: Past, present and future. Med. Eng. Phys. 2019;72:55–65. doi: 10.1016/j.medengphy.2019.08.005. PubMed DOI
Sugano N. Computer Assisted Orthopaedic Surgery for Hip and Knee: Current State of the Art in Clinical Application and Basic Research. Springer; Berlin/Heidelberg, Germany: 2018. pp. 1–206.
Karunaratne S., Duan M., Pappas E., Fritsch B., Boyle R., Gupta S., Stalley P., Horsley M., Steffens D. The effectiveness of robotic hip and knee arthroplasty on patient-reported outcomes: A systematic review and meta-analysis. Int. Orthop. 2019;43:1283–1295. doi: 10.1007/s00264-018-4140-3. PubMed DOI
Sugano N. Computer-assisted orthopedic surgery. J. Orthop. Sci. 2003;8:442–448. doi: 10.1007/s10776-002-0623-6. PubMed DOI
Simulation of Orbital Fractures Using Experimental and Mathematical Approaches: A Pilot Study