MR Microsurgical Suture Training System with Level-Appropriate Support
Authors: 
Yuka Tashiro, 
Shio Miyafuji, Yusuke Kojima, Satoshi Kiyofuji, Taichi Kin, Takeo Igarashi, 
Hideki KoikeAuthors Info & Claims
Article No.: 460, Pages 1 - 19
Abstract
The integration of advanced technologies in healthcare necessitates the development of systems accommodating the daily routines in medical practices. Neurosurgeons, in particular, require extensive practice in microsurgical suturing in the long term, even in the busy routine of a medical practice. This study collaboratively developed a Mixed Reality system with neurosurgeons to support self-training in microscopic suturing. Based on the neurosurgeons’ opinions, we implemented a level-appropriate microsurgical suture training system. For novices, the system offers shadow-matching training to support the practice of precise movements under the high-sensitivity environment of the microscope. For intermediates, it provides a real-time feedback system, which allows users to practice attention to details. Evaluation involved testing the novice system on students with no medical background and the intermediate system on neurosurgery residents. The effectiveness of the system was demonstrated through the experimental results and subsequent discussion.
Supplemental Material
MP4 File - Video Preview
Video Preview
- Download
- 18.62 MB
- Download
- 117.84 MB
- Transcript
MP4 File - The video of a summary of our paper
This video is a summary of our paper about 3 minutes long.
- Download
- 49.07 MB
- Transcript
References
[1]
Ehsan Azimi, Zhiyuan Niu, Maia Stiber, Nicholas Greene, Ruby Liu, Camilo Molina, Judy Huang, Chien-Ming Huang, and Peter Kazanzides. 2020. An Interactive Mixed Reality Platform for Bedside Surgical Procedures. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, and Leo Joskowicz (Eds.). Springer International Publishing, Cham, 65–75.
[2]
Anne Kathrine Petersen Bach, Trine Munch Nørgaard, Jens Christian Brok, and Niels van Berkel. 2023. “If I Had All the Time in the World”: Ophthalmologists’ Perceptions of Anchoring Bias Mitigation in Clinical AI Support. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (, Hamburg, Germany,) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 16, 14 pages. https://doi.org/10.1145/3544548.3581513
[3]
Mohamed Benmahdjoub, Abdullah Thabit, Marie-Lise C. van Veelen, Wiro J. Niessen, Eppo B. Wolvius, and Theo van Walsum. 2023. Evaluation of AR visualization approaches for catheter insertion into the ventricle cavity. IEEE Transactions on Visualization and Computer Graphics 29, 5 (2023), 2434–2445. https://doi.org/10.1109/TVCG.2023.3247042
[4]
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020).
[5]
John Brooke. 1996. SUS – a quick and dirty usability scale. 189–194.
[6]
Eleanor R. Burgess, Ivana Jankovic, Melissa Austin, Nancy Cai, Adela Kapuścińska, Suzanne Currie, J. Marc Overhage, Erika S Poole, and Jofish Kaye. 2023. Healthcare AI Treatment Decision Support: Design Principles to Enhance Clinician Adoption and Trust. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (, Hamburg, Germany,) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 15, 19 pages. https://doi.org/10.1145/3544548.3581251
[7]
Carrie J. Cai, Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, Greg S. Corrado, Martin C. Stumpe, and Michael Terry. 2019. Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3290605.3300234
[8]
Carrie J. Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019. "Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 104 (nov 2019), 24 pages. https://doi.org/10.1145/3359206
[9]
Francisco Maria Calisto, João Fernandes, Margarida Morais, Carlos Santiago, João Maria Abrantes, Nuno Nunes, and Jacinto C. Nascimento. 2023. Assertiveness-Based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 13, 20 pages. https://doi.org/10.1145/3544548.3580682
[10]
Francisco Maria Calisto, Nuno Nunes, and Jacinto C. Nascimento. 2022. Modeling adoption of intelligent agents in medical imaging. International Journal of Human-Computer Studies 168 (2022), 102922. https://doi.org/10.1016/j.ijhcs.2022.102922
[11]
Gabriele Campanella, Matthew G. Hanna, Luke Geneslaw, Allen Miraflor, Vitor Werneck Krauss Silva, Klaus J. Busam, Edi Brogi, Victor E. Reuter, David S. Klimstra, and Thomas J. Fuchs. 2019. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine 25, 8 (01 Aug 2019), 1301–1309. https://doi.org/10.1038/s41591-019-0508-1
[12]
Ching-Yi Chang, Han-Yu Sung, Jong-Long Guo, Bieng-Yi Chang, and Fan-Ray Kuo. 2022. Effects of spherical video-based virtual reality on nursing students’ learning performance in childbirth education training. Interactive Learning Environments 30, 3 (2022), 400–416. https://doi.org/10.1080/10494820.2019.1661854 arXiv:https://doi.org/10.1080/10494820.2019.1661854
[13]
Alessandro De Mauro, Joerg Raczkowsky, Marc-Eric Halatsch, and Heinz Wörn. 2009. Mixed Reality Neurosurgical Microscope for Training and Intra-operative Purposes. 542–549. https://doi.org/10.1007/978-3-642-02771-0_60
[14]
Hazel Doughty, Dima Damen, and Walterio Mayol-Cuevas. 2018. Who’s Better? Who’s Best? Pairwise Deep Ranking for Skill Determination. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6057–6066. https://doi.org/10.1109/CVPR.2018.00634
[15]
Hiroyuki Egi, Masazumi Okajima, Masanori Yoshimitsu, Satoshi Ikeda, Yoshihiro Miyata, Hirokazu Masugami, Tomohiro Kawahara, Yuichi Kurita, Makoto Kaneko, and Toshimasa Asahara. 2008. Objective assessment of endoscopic surgical skills by analyzing direction-dependent dexterity using the Hiroshima University Endoscopic Surgical Assessment Device (HUESAD). Surgery Today 38, 8 (01 Aug 2008), 705–710. https://doi.org/10.1007/s00595-007-3696-0
[16]
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen A. W. M. van der Laak, and the CAMELYON16 Consortium. 2017. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA 318, 22 (12 2017), 2199–2210. https://doi.org/10.1001/jama.2017.14585 arXiv:https://jamanetwork.com/journals/jama/articlepdf/2665774/jama_ehteshami_bejnordi_2017_oi_170113.pdf
[17]
Sangjun Eom, David Sykes, Shervin Rahimpour, and Maria Gorlatova. 2022. NeuroLens: Augmented Reality-based Contextual Guidance through Surgical Tool Tracking in Neurosurgery. In 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 355–364. https://doi.org/10.1109/ISMAR55827.2022.00051
[18]
Shaun Gallagher. 2000. Philosophical conceptions of the self: implications for cognitive science. Trends in Cognitive Sciences 4, 1 (2000), 14–21. https://doi.org/10.1016/S1364-6613(99)01417-5
[19]
Hongyan Gu, Chunxu Yang, Mohammad Haeri, Jing Wang, Shirley Tang, Wenzhong Yan, Shujin He, Christopher Kazu Williams, Shino Magaki, and Xiang ’Anthony’ Chen. 2023. Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation System(CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 349, 19 pages. https://doi.org/10.1145/3544548.3580694
[20]
Nishan Gunawardena, Michael Matscheko, Bernhard Anzengruber, Alois Ferscha, Martin Schobesberger, Andreas Shamiyeh, Bettina Klugsberger, and Peter Solleder. 2019. The Effect of Expertise on Gaze Behaviour in Laparoscopic Cholecystectomy. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 7123–7127. https://doi.org/10.1109/EMBC.2019.8857612
[21]
Kanako Harada, Akio Morita, Yoshiaki Minakawa, Young Min Baek, Shigeo Sora, Naohiko Sugita, Toshikazu Kimura, Rokuya Tanikawa, Tatsuya Ishikawa, and Mamoru Mitsuishi. 2015. Assessing Microneurosurgical Skill with Medico-Engineering Technology. World Neurosurgery 84, 4 (2015), 964–971. https://doi.org/10.1016/j.wneu.2015.05.033
[22]
Patrick Haubruck, Felix Nickel, Julian Ober, Tilman Walker, Christian Bergdolt, Mirco Friedrich, Beat Peter Müller-Stich, Franziska Forchheim, Christian Fischer, Gerhard Schmidmaier, and Michael C Tanner. 2018. Evaluation of App-Based Serious Gaming as a Training Method in Teaching Chest Tube Insertion to Medical Students: Randomized Controlled Trial. J Med Internet Res 20, 5 (21 May 2018), e195. https://doi.org/10.2196/jmir.9956
[23]
Frouke Hermens, Rhona Flin, and Irfan Ahmed. 2013. Eye movements in surgery : A literature review. Journal of Eye Movement Research 6, 4 (Nov. 2013). https://doi.org/10.16910/jemr.6.4.4
[24]
Derek Kwun-hong Ho. 2019. Using smartphone-delivered stereoscopic vision in microsurgery: a feasibility study. Eye 33, 6 (01 Jun 2019), 953–956. https://doi.org/10.1038/s41433-019-0356-8
[25]
Biyun Huang and Khe Foon Hew. 2015. Do points, badges and leaderboard increase learning and activity: A quasi-experiment on the effects of gamification. In Proceedings of the 23rd International Conference on Computers in Education. Society for Computer in Education Hangzhou, China, 275–280. http://hdl.handle.net/10722/223925
[26]
Wouter M. IJgosse, Harry van Goor, and Jan-Maarten Luursema. 2018. Saving robots improves laparoscopic performance: transfer of skills from a serious game to a virtual reality simulator. Surgical Endoscopy 32, 7 (01 Jul 2018), 3192–3199. https://doi.org/10.1007/s00464-018-6036-0
[27]
Tomohiro Inoue, Naoto Kunii, Atsushi Kumakiri, Ryohei Otani, Akira Tamura, Isamu Saito, and Kazuo Tsutsumi. 2009. The Role of 10–0 Suturing Training under Desktype Microscope for the Mastery of Cerebrovascular Surgery: The Effectiveness of 80,000 Stitches. Surgery for Cerebral Stroke 37, 4 (2009), 247–252. https://doi.org/10.2335/scs.37.247
[28]
Tomohiro Inoue, Kazuo Tsutsumi, Shinobu Adachi, Shota Tanaka, Kuniaki Saito, and Naoto Kunii. 2006. Effectiveness of suturing training with 10-0 nylon under fixed and maximum magnification (×20) using desk type microscope. Surgical Neurology 66, 2 (2006), 183–187. https://doi.org/10.1016/j.surneu.2005.11.064
[29]
Maia Jacobs, Jeffrey He, Melanie F. Pradier, Barbara Lam, Andrew C. Ahn, Thomas H. McCoy, Roy H. Perlis, Finale Doshi-Velez, and Krzysztof Z. Gajos. 2021. Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (, Yokohama, Japan,) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 659, 14 pages. https://doi.org/10.1145/3411764.3445385
[30]
Jesper Kers, Roman D Bülow, Barbara M Klinkhammer, Gerben E Breimer, Francesco Fontana, Adeyemi Adefidipe Abiola, Rianne Hofstraat, Garry L Corthals, Hessel Peters-Sengers, Sonja Djudjaj, Saskia von Stillfried, David L Hölscher, Tobias T Pieters, Arjan D van Zuilen, Frederike J Bemelman, Azam S Nurmohamed, Maarten Naesens, Joris J T H Roelofs, Sandrine Florquin, Jürgen Floege, Tri Q Nguyen, Jakob N Kather, and Peter Boor. 2022. Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study. The Lancet Digital Health 4, 1 (2022), e18–e26. https://doi.org/10.1016/S2589-7500(21)00211-9
[31]
Talha Khan, Edward G. Andrews, Paul A. Gardner, Arka N. Mallela, Jeffrey R. Head, Joseph C. Maroon, Georgios A. Zenonos, Dmitriy Babichenko, and Jacob T. Biehl. 2022. AR in the OR: Exploring Use of Augmented Reality to Support Endoscopic Surgery. In Proceedings of the 2022 ACM International Conference on Interactive Media Experiences (Aveiro, JB, Portugal) (IMX ’22). Association for Computing Machinery, New York, NY, USA, 267–270. https://doi.org/10.1145/3505284.3532970
[32]
Navaneeth Kamballur Kottayil, Rositsa Bogdanova, Irene Cheng, Bin Zheng, and Anup Basu. 2016. Investigation of gaze patterns in multi view laparoscopic surgery. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 4031–4034. https://doi.org/10.1109/EMBC.2016.7591611
[33]
Bettina Laugwitz, Theo Held, and Martin Schrepp. 2008. Construction and Evaluation of a User Experience Questionnaire. USAB 2008 5298, 63–76. https://doi.org/10.1007/978-3-540-89350-9_6
[34]
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, and Sergi Bermúdez i Badia. 2020. Co-Design and Evaluation of an Intelligent Decision Support System for Stroke Rehabilitation Assessment. Proc. ACM Hum.-Comput. Interact. 4, CSCW2, Article 156 (oct 2020), 27 pages. https://doi.org/10.1145/3415227
[35]
Jie Li, Guo Chen, Huib de Ridder, and Pablo Cesar. 2020. Designing a Social VR Clinic for Medical Consultations(CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–9. https://doi.org/10.1145/3334480.3382836
[36]
Martin Lindvall, Claes Lundström, and Jonas Löwgren. 2021. Rapid Assisted Visual Search: Supporting Digital Pathologists with Imperfect AI. In 26th International Conference on Intelligent User Interfaces (College Station, TX, USA) (IUI ’21). Association for Computing Machinery, New York, NY, USA, 504–513. https://doi.org/10.1145/3397481.3450681
[37]
Juping Liu, Di Wu, Xinjun Ren, and Xiaorong Li. 2021. Clinical experience of using the NGENUITY three-dimensional surgery system in ophthalmic surgical procedures. Acta Ophthalmologica 99, 1 (2021), e101–e108. https://doi.org/10.1111/aos.14518 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/aos.14518
[38]
Cristian Lorenzini, Claudia Faita, Marcello Carrozzino, Franco Tecchia, and Massimo Bergamasco. 2015. VR-Based Serious Game Designed for Medical Ethics Training. In Augmented and Virtual Reality, Lucio Tommaso De Paolis and Antonio Mongelli (Eds.). Springer International Publishing, Cham, 220–232.
[39]
David G Lowe. 2004. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 2 (Nov. 2004), 91–110.
[40]
Roux Ludovic, Racoceanu Daniel, Loménie Nicolas, Kulikova Maria, Irshad Humayun, Klossa Jacques, Capron Frédérique, Genestie Catherine, Le Naour Gilles, and Gurcan Metin N. 2013. Mitosis detection in breast cancer histological images An ICPR 2012 contest. Journal of Pathology Informatics 4, 1 (2013), 8. https://doi.org/10.4103/2153-3539.112693
[41]
Faraz Mahmood, Eitezaz Mahmood, Robert Gregory Dorfman, John Mitchell, Feroze-Udin Mahmood, Stephanie B. Jones, and Robina Matyal. 2018. Augmented Reality and Ultrasound Education: Initial Experience. Journal of Cardiothoracic and Vascular Anesthesia 32, 3 (2018), 1363–1367. https://doi.org/10.1053/j.jvca.2017.12.006
[42]
Ricardo Marian-Magaña, Marcos V Sangrador-Deitos, Gerardo Y Guinto-Nishimura, Daniel Ballesteros-Herrera, Gerardo Cano-Velazquez, Obet J Canela-Calderón, Jorge Ríos-Zermeño, Jorge F Aragon-Arreola, and Juan L Gómez-Amador. 2022. Microsurgical training through laboratory experience: A step-by-step practical guideline. Interdisciplinary Neurosurgery 27 (2022), 101400. https://doi.org/10.1016/j.inat.2021.101400
[43]
Akira Matsuda, Toru Okuzono, Hiromi Nakamura, Hideaki Kuzuoka, and Jun Rekimoto. 2021. A Surgical Scene Replay System for Learning Gastroenterological Endoscopic Surgery Skill by Multiple Synchronized-Video and Gaze Representation. Proc. ACM Hum.-Comput. Interact. 5, EICS, Article 204 (may 2021), 22 pages. https://doi.org/10.1145/3461726
[44]
Mikihito Matsuura, Shio Miyafuji, Erwin Wu, Satoshi Kiyofuji, Taichi Kin, Takeo Igarashi, and Hideki Koike. 2021. CV-Based Analysis for Microscopic Gauze Suturing Training. In Augmented Humans Conference 2021 (Rovaniemi, Finland) (AHs’21). Association for Computing Machinery, New York, NY, USA, 169–173. https://doi.org/10.1145/3458709.3458991
[45]
Nadine Marie Moacdieh, Michel Dibo, Zeina Halabi, and Jumana Antoun. 2023. Eye Tracking to Evaluate the Effectiveness of Electronic Medical Record Training. In Proceedings of the 2023 Symposium on Eye Tracking Research and Applications (Tubingen, Germany) (ETRA ’23). Association for Computing Machinery, New York, NY, USA, Article 5, 7 pages. https://doi.org/10.1145/3588015.3588418
[46]
Takayuki Okamoto, Takashi Ohnishi, Hiroshi Kawahira, Olga Dergachyava, Pierre Jannin, and Hideaki Haneishi. 2019. Real-time identification of blood regions for hemostasis support in laparoscopic surgery. Signal, Image and Video Processing 13, 2 (01 Mar 2019), 405–412. https://doi.org/10.1007/s11760-018-1369-7
[47]
Manuel Rebol, Krzysztof Pietroszek, Neal Sikka, Claudia Ranniger, Colton Hood, Adam Rutenberg, Puja Sasankan, and Christian Gütl. 2023. Evaluating Augmented Reality Communication: How Can We Teach Procedural Skill in AR?(VRST ’23). Association for Computing Machinery, New York, NY, USA, Article 20, 11 pages. https://doi.org/10.1145/3611659.3615685
[48]
Manuel Rebol, Alexander Steinmaurer, Florian Gamillscheg, Krzysztof Pietroszek, Christian Gütl, Claudia Ranniger, Colton Hood, Adam Rutenberg, and Neal Sikka. 2023. CPR Emergency Assistance Through Mixed Reality Communication. In Augmented Intelligence and Intelligent Tutoring Systems, Claude Frasson, Phivos Mylonas, and Christos Troussas (Eds.). Springer Nature Switzerland, Cham, 415–429.
[49]
Anke Verena Reinschluessel, Thomas Muender, Roland Fischer, Valentin Kraft, Verena Nicole Uslar, Dirk Weyhe, Andrea Schenk, Gabriel Zachmann, Tanja Döring, and Rainer Malaka. 2023. Versatile Immersive Virtual and Augmented Tangible OR – Using VR, AR and Tangibles to Support Surgical Practice. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA ’23). Association for Computing Machinery, New York, NY, USA, Article 477, 5 pages. https://doi.org/10.1145/3544549.3583895
[50]
Maximilian Rettinger and Gerhard Rigoll. 2022. Defuse the Training of Risky Tasks: Collaborative Training in XR. In 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 695–701. https://doi.org/10.1109/ISMAR55827.2022.00087
[51]
Pedro Rodrigues, Francisco Nicolau, Martim Norte, Ezequiel Zorzal, João Botelho, Vanessa Machado, Luís Proença, Ricardo Alves, Carlos Zagalo, Daniel Simões Lopes, and José João Mendes. 2023. Preclinical dental students self-assessment of an improved operative dentistry virtual reality simulator with haptic feedback. Scientific Reports 13, 1 (17 Feb 2023), 2823. https://doi.org/10.1038/s41598-023-29537-5
[52]
Daniel Roth, Kevin Yu, Frieder Pankratz, Gleb Gorbachev, Andreas Keller, Marc Lazarovici, Dirk Wilhelm, Simon Weidert, Nassir Navab, and Ulrich Eck. 2021. Real-time Mixed Reality Teleconsultation for Intensive Care Units in Pandemic Situations. In 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). 693–694. https://doi.org/10.1109/VRW52623.2021.00229
[53]
Ravi Sharma and Ashish Suri. 2022. Microsurgical suturing assessment scores: a systematic review. Neurosurgical Review 45, 1 (2022), 119–124.
[54]
Jotaro Shigeyama and Thijs Roumen. 2021. SpeechIOForUnity. https://github.com/HassoPlattnerInstituteHCI/SpeechIOForUnity.
[55]
Xuetong Sun, Sarah B. Murthi, Gary Schwartzbauer, and Amitabh Varshney. 2020. High-Precision 5DoF Tracking and Visualization of Catheter Placement in EVD of the Brain Using AR. 1, 2, Article 9 (mar 2020), 18 pages. https://doi.org/10.1145/3365678
[56]
Matthias Süncksen, Henner Bendig, Michael Teistler, Markus Wagner, Oliver Johannes Bott, and Klaus Dresing. 2018. Gamification and virtual reality for teaching mobile x-ray imaging. In 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH). 1–7. https://doi.org/10.1109/SeGAH.2018.8401364
[57]
Mikinobu Takeuchi, Nakamasa Hayashi, Hideo Hamada, Nobuhisa Matsumura, Hisao Nishijo, and Shunro Endo. 2008. A new training method to improve deep microsurgical skills using a mannequin head. Microsurgery: Official Journal of the International Microsurgical Society and the European Federation of Societies for Microsurgery 28, 3 (2008), 168–170.
[58]
Yuka Tashiro, Shio Miyafuji, Dong-Hyun Hwang, Satoshi Kiyofuji, Taichi Kin, Takeo Igarashi, and Hideki Koike. 2023. GAuze-MIcrosuture-FICATION: Gamification in Microsuture Training with Real-Time Feedback. In Proceedings of the Augmented Humans International Conference 2023 (Glasgow, United Kingdom) (AHs ’23). Association for Computing Machinery, New York, NY, USA, 15–26. https://doi.org/10.1145/3582700.3582704
[59]
David Tellez, Maschenka Balkenhol, Irene Otte-Höller, Rob van de Loo, Rob Vogels, Peter Bult, Carla Wauters, Willem Vreuls, Suzanne Mol, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak, and Francesco Ciompi. 2018. Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks. IEEE Transactions on Medical Imaging 37, 9 (2018), 2126–2136. https://doi.org/10.1109/TMI.2018.2820199
[60]
Puxun Tu, Yao Gao, Abel J Lungu, Dongyuan Li, Huixiang Wang, and Xiaojun Chen. 2021. Augmented reality based navigation for distal interlocking of intramedullary nails utilizing Microsoft HoloLens 2. Computers in Biology and Medicine 133 (2021), 104402. https://doi.org/10.1016/j.compbiomed.2021.104402
[61]
Jakob Carl Uhl, Helmut Schrom-Feiertag, Georg Regal, Katja Gallhuber, and Manfred Tscheligi. 2023. Tangible Immersive Trauma Simulation: Is Mixed Reality the next Level of Medical Skills Training?(CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 513, 17 pages. https://doi.org/10.1145/3544548.3581292
[62]
Ultralytics. 2023. Yolov8. https://docs.ultralytics.com.
[63]
Tristan PC van Doormaal, Jesse AM van Doormaal, and Tom Mensink. 2019. Clinical accuracy of holographic navigation using point-based registration on augmented-reality glasses. Operative Neurosurgery 17, 6 (2019), 588.
[64]
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikael Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Viana, Vassili Kovalev, Vitali Liauchuk, Hady Ahmady Phoulady, Talha Qaiser, Simon Graham, Nasir Rajpoot, Erik Sjöblom, Jesper Molin, Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Zhipeng Jia, Eric I-Chao Chang, Yan Xu, Andrew H. Beck, Paul J. van Diest, and Josien P.W. Pluim. 2019. Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Medical Image Analysis 54 (2019), 111–121. https://doi.org/10.1016/j.media.2019.02.012
[65]
Ziheng Wang and Ann Majewicz Fey. 2018. Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. International Journal of Computer Assisted Radiology and Surgery 13, 12 (01 Dec 2018), 1959–1970. https://doi.org/10.1007/s11548-018-1860-1
[66]
Nadir Weibel, Danilo Gasques, Janet Johnson, Thomas Sharkey, Zhuoqun Robin Xu, Xinming Zhang, Enrique Zavala, Michael Yip, and Konrad Davis. 2020. ARTEMIS: Mixed-Reality Environment for Immersive Surgical Telementoring. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–4. https://doi.org/10.1145/3334480.3383169
[67]
Yao Xie, Melody Chen, David Kao, Ge Gao, and Xiang ’Anthony’ Chen. 2020. CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376807
[68]
Masayoshi Yamada, Yutaka Saito, Hitoshi Imaoka, Masahiro Saiko, Shigemi Yamada, Hiroko Kondo, Hiroyuki Takamaru, Taku Sakamoto, Jun Sese, Aya Kuchiba, Taro Shibata, and Ryuji Hamamoto. 2019. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Scientific Reports 9, 1 (08 Oct 2019), 14465. https://doi.org/10.1038/s41598-019-50567-5
[69]
Qian Yang, Aaron Steinfeld, and John Zimmerman. 2019. Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3290605.3300468
[70]
Jungo Yasuda, Tomoyoshi Okamoto, Shinji Onda, Shuuichi Fujioka, Katsuhiko Yanaga, Naoki Suzuki, and Asaki Hattori. 2020. Application of image-guided navigation system for laparoscopic hepatobiliary surgery. Asian Journal of Endoscopic Surgery 13, 1 (2020), 39–45. https://doi.org/10.1111/ases.12696
[71]
Bin Zheng, Xianta Jiang, Roman Bednarik, and M. Stella Atkins. 2016. Gaze characteristics of video watching in a surgical setting. In 2016 IEEE Second Workshop on Eye Tracking and Visualization (ETVIS). 11–15. https://doi.org/10.1109/ETVIS.2016.7851158
[72]
Paul Zikas, Antonis Protopsaltis, Nick Lydatakis, Mike Kentros, Stratos Geronikolakis, Steve Kateros, Manos Kamarianakis, Giannis Evangelou, Achilleas Filippidis, Eleni Grigoriou, Dimitris Angelis, Michail Tamiolakis, Michael Dodis, George Kokiadis, John Petropoulos, Maria Pateraki, and George Papagiannakis. 2023. MAGES 4.0: Accelerating the World’s Transition to VR Training and Democratizing the Authoring of the Medical Metaverse. IEEE Computer Graphics and Applications 43, 2 (2023), 43–56. https://doi.org/10.1109/MCG.2023.3242686
Index Terms
- MR Microsurgical Suture Training System with Level-Appropriate Support
Index terms have been assigned to the content through auto-classification.
Recommendations
MR MANE: MR Microsurgical Suturing Skill Acquisition for Novice Using Imitation of Example
AVI '24: Proceedings of the 2024 International Conference on Advanced Visual InterfacesThe training of microsurgical suturing techniques in neurosurgery requires a considerable amount of time, and there is a need for training methods that enhance efficiency. Especially for novices, imitation of examples and receiving feedback from ...
GAuze-MIcrosuture-FICATION: Gamification in Microsuture training with real-time feedback
AHs '23: Proceedings of the Augmented Humans International Conference 2023Microscopic suturing in neurosurgery is a challenging medical technique that requires time to master. Since skilled surgeons are too busy to spend much time with novice surgeons, novice surgeons need to train alone in monotonous tasks to acquire skills. ...
Introducing a Concept of Gamification to Microscopic Suturing Training
AHs '22: Proceedings of the Augmented Humans International Conference 2022Microscopic suturing in neurosurgery is a difficult medical technique that requires time to master. Since skilled neurosurgeons are busy and have little time to train novice surgeons, novice surgeons must train alone in monotonous tasks to acquire the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2024
18961 pages
ISBN:9798400703300
DOI:10.1145/3613904
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 May 2024
Check for updates
Badges
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- JST CREST
- JSPS KAKENHI
Conference
Acceptance Rates
Overall Acceptance Rate 6,199 of 26,314 submissions, 24%
Upcoming Conference
CHI '25
- Sponsor:
- sigchi
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 513Total Downloads
- Downloads (Last 12 months)513
- Downloads (Last 6 weeks)87
Reflects downloads up to 19 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderFull Text
View this article in Full Text.
Full TextHTML Format
View this article in HTML Format.
HTML Format