Assessment of changes in vessel area during needle manipulation in microvascular anastomosis using a deep learning-based semantic segmentation algorithm: A pilot study

Aoun SG, El Ahmadieh TY, El Tecle NE, Daou MR, Adel JG, Park CS, Batjer HH, Bendok BR (2015) A pilot study to assess the construct and face validity of the Northwestern Objective Microanastomosis Assessment Tool. J Neurosurg 123:103–109. https://doi.org/10.3171/2014.12.JNS131814

Article  PubMed  Google Scholar 

Baghdadi A, Lama S, Singh R, Hoshyarmanesh H, Razmi M, Sutherland GR (2021) A data-driven performance dashboard for surgical dissection. Sci Rep 2021 111 11:1–13. https://doi.org/10.1038/s41598-021-94487-9

Article  CAS  Google Scholar 

Birkmeyer JD, Finks JF, O’Reilly A, Oerline M, Carlin AM, Nunn AR, Dimick J, Banerjee M, Birkmeyer NJO (2013) Surgical skill and complication rates after bariatric surgery. N Engl J Med 369:1434–1442. https://doi.org/10.1056/NEJMsa1300625

Article  CAS  PubMed  Google Scholar 

Castillo-Segura P, Fernández-Panadero C, Alario-Hoyos C, Muñoz-Merino PJ, Delgado Kloos C (2021) Objective and automated assessment of surgical technical skills with IoT systems: a systematic literature review. Artif Intell Med 112:102007

Article  PubMed  Google Scholar 

Curtis NJ, Dennison G, Brown CSB, Hewett PJ, Hanna GB, Stevenson ARL, Francis NK (2019) Clinical evaluation of Intraoperative Near misses in laparoscopic rectal Cancer surgery. Ann Surg XX 1. https://doi.org/10.1097/sla.0000000000003452

Darzi A, Smith S, Taffinder N (1999) Assessing operative skill. Br Med J 318:887–888

Article  CAS  Google Scholar 

Davids J, Makariou SG, Ashrafian H, Darzi A, Marcus HJ, Giannarou S (2021) Automated vision-based Microsurgical Skill Analysis in Neurosurgery using deep learning: development and preclinical validation. World Neurosurg 149:e669–e686. https://doi.org/10.1016/j.wneu.2021.01.117

Article  PubMed  Google Scholar 

Fecso AB, Szasz P, Kerezov G, Grantcharov TP (2017) The effect of technical performance on patient outcomes in surgery. Ann Surg 265:492–501. https://doi.org/10.1097/SLA.0000000000001959

Article  PubMed  Google Scholar 

Fujimura M, Tominaga T (2021) Flow-augmentation bypass for moyamoya disease. J Neurosurg Sci 65:277–286

Article  PubMed  Google Scholar 

Fujimura M, Kaneta T, Tominaga T (2008) Efficacy of superficial temporal artery-middle cerebral artery anastomosis with routine postoperative cerebral blood flow measurement during the acute stage in childhood moyamoya disease. Child’s Nerv Syst 24:827–832. https://doi.org/10.1007/s00381-007-0551-y

Article  Google Scholar 

Fujimura M, Tominaga T, Kuroda S, Takahashi JC, Endo H, Ogasawara K, Miyamoto S (2022) 2021 Japanese guidelines for the management of Moyamoya Disease: guidelines from the Research Committee on Moyamoya Disease and Japan Stroke Society. Neurol Med Chir (Tokyo) 62:165–170. https://doi.org/10.2176/jns-nmc.2021-0382

Article  PubMed  Google Scholar 

Ghasemloonia A, Maddahi Y, Zareinia K, Lama S, Dort JC, Sutherland GR (2017) Surgical Skill Assessment using motion quality and smoothness. J Surg Educ 74:295–305. https://doi.org/10.1016/j.jsurg.2016.10.006

Article  PubMed  Google Scholar 

Goff BA, Lentz GM, Lee D, Houmard B, Mandel LS (2000) Development of an objective structured assessment of technical skills for obstetric and gynecology residents. Obstet Gynecol 96:146–150. https://doi.org/10.1016/S0029-7844(00)00829-2

Article  CAS  PubMed  Google Scholar 

Grober ED, Roberts M, Shin EJ, Mahdi M, Bacal V (2010) Intraoperative assessment of technical skills on live patients using economy of hand motion: establishing learning curves of surgical competence. Am J Surg 199:81–85. https://doi.org/10.1016/j.amjsurg.2009.07.033

Article  PubMed  Google Scholar 

Harada K, Morita A, Minakawa Y, Baek YM, Sora S, Sugita N, Kimura T, Tanikawa R, Ishikawa T, Mitsuishi M (2015) Assessing Microneurosurgical Skill with Medico-Engineering Technology. World Neurosurg 84:964–971. https://doi.org/10.1016/j.wneu.2015.05.033

Article  PubMed  Google Scholar 

Jo K, Choi Y, Choi J, Chung JW (2019) Robust real-time detection of Laparoscopic instruments in Robot surgery using Convolutional neural networks with motion Vector Prediction. Appl Sci 9:2865. https://doi.org/10.3390/app9142865

Article  Google Scholar 

Martin JA, Regehr G, Reznick R, Macrae H, Murnaghan J, Hutchison C, Brown M (1997) Objective structured assessment of technical skill (OSATS) for surgical residents. Br J Surg 84:273–278. https://doi.org/10.1002/bjs.1800840237

Article  CAS  PubMed  Google Scholar 

Mascitelli JR, Yoon S, Cole TS, Kim H, Lawton MT (2019) Does eloquence subtype influence outcome following arteriovenous malformation surgery? J Neurosurg 131:876–883. https://doi.org/10.3171/2018.4.JNS18403

Article  Google Scholar 

McBeth PB, Louw DF, Yang F, Sutherland GR (2005) Quantitative measures of performance in microvascular anastomoses. Comput Aided Surg 10:173–180. https://doi.org/10.3109/10929080500229694

Article  PubMed  Google Scholar 

McGoldrick RB, Davis CR, Paro J, Hui K, Nguyen D, Lee GK (2015) Motion analysis for microsurgical training: objective measures of dexterity, economy of movement, and ability. Plast Reconstr Surg 136:231e–240e. https://doi.org/10.1097/PRS.0000000000001469

Article  CAS  PubMed  Google Scholar 

Neilsen PE, Foglia LM, Mandel LS, Chow GE (2003) Objective structured assessment of technical skills for episiotomy repair. Am J Obstet Gynecol 189:1257–1260. https://doi.org/10.1067/S0002-9378(03)00812-3

Article  Google Scholar 

Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12:145–151. https://doi.org/10.1016/S0893-6080(98)00116-6

Article  CAS  PubMed  Google Scholar 

Reznick RK (1993) Teaching and testing technical skills. Am J Surg 165:358–361. https://doi.org/10.1016/S0002-9610(05)80843-8

Article  CAS  PubMed  Google Scholar 

Sarkiss CA, Philemond S, Lee J, Sobotka S, Holloway TD, Moore MM, Costa AB, Gordon EL, Bederson JB (2016) Neurosurgical Skills Assessment: measuring technical proficiency in neurosurgery residents through intraoperative video evaluations. World Neurosurg 89:1–8. https://doi.org/10.1016/j.wneu.2015.12.052

Article  PubMed  Google Scholar 

Sugimori H, Sugiyama T, Nakayama N, Yamashita A, Ogasawara K (2020) Development of a deep learning-based algorithm to detect the distal end of a Surgical Instrument. Appl Sci 10:4245. https://doi.org/10.3390/app10124245

Article  CAS  Google Scholar 

Sugiyama T (2017) Mastering Intracranial Microvascular anastomoses -Basic techniques and Surgical pearls. MEDICUS SHUPPAN, Publishers Co., Ltd

Google Scholar 

Sugiyama T, Kazumata K, Asaoka K, Osanai T, Shimbo D, Uchida K, Yokoyama Y, Nakayama N, Itamoto K, Houkin K (2015) Reappraisal of microsurgical revascularization for anterior circulation ischemia in patients with progressive stroke. World Neurosurg 84:1579–1588. https://doi.org/10.1016/j.wneu.2015.07.053

Article  PubMed  Google Scholar 

Sugiyama T, Gan LS, Zareinia K, Lama S, Sutherland GR (2017) Tool-Tissue Interaction Forces in Brain arteriovenous malformation surgery. World Neurosurg 102:221–228. https://doi.org/10.1016/j.wneu.2017.03.006

Article  PubMed  Google Scholar 

Sugiyama T, Lama S, Gan LS, Maddahi Y, Zareinia K, Sutherland GR (2018) Forces of tool-tissue interaction to assess surgical skill level. JAMA Surg 153:234–242. https://doi.org/10.1001/jamasurg.2017.4516

Article  PubMed  Google Scholar 

Sugiyama T, Nakamura T, Ito Y, Tokairin K, Kazumata K, Nakayama N, Houkin K (2019) A pilot study on measuring tissue motion during carotid surgery using video-based analyses for the Objective Assessment of Surgical Performance. World J Surg 43:2309–2319. https://doi.org/10.1007/s00268-019-05018-7

Article  PubMed  Google Scholar 

Sugiyama T, Ito M, Sugimori H, Tang M, Nakamura T, Ogasawara K, Matsuzawa H, Nakayama N, Lama S, Sutherland GR, Fujimura M (2023) Tissue acceleration as a Novel Metric for Surgical Performance during Carotid Endarterectomy. Oper Neurosurg 25:343–352. https://doi.org/10.1227/ons.0000000000000815

Article  Google Scholar 

Sugiyama T, Sugimori H, Tang M, Ito Y, Gekka M, Uchino H, Ito M, Ogasawara K, Fujimura M (2024) Deep learning-based video-analysis of instrument motion in microvascular anastomosis training. Acta Neurochir (Wien) 166:6. https://doi.org/10.1007/s00701-024-05896-4

Article  PubMed  Google Scholar 

Takagi Y, Kikuta KI, Nozaki K, Hashimoto N (2007) Histological features of middle cerebral arteries from patients treated for moyamoya disease. Neurol Med Chir (Tokyo) 47:1–4. https://doi.org/10.2176/nmc.47.1

Article  PubMed  Google Scholar 

Tang M, Kawahira S, Nomura N, Yamamoto T (2020) Torque property of titanium alloy cerebral aneurysm clips in a magnetic resonance scanner. J Mater Sci Mater Med 31:6. https://doi.org/10.1007/s10856-019-6329-4

Article  CAS  Google Scholar 

Tanimoto TT (1958) An Elementary Mathematical Theory of classification and prediction. Proc IBM Intern Rep 1–11

Zareinia K, Maddahi Y, Gan LS, Ghasemloonia A, Lama S, Sugiyama T, Yang FW, Sutherland GR (2016) A force-sensing bipolar forceps to Quantify Tool-tissue Interaction forces in Microsurgery. IEEE/ASME Trans Mechatronics 21:2365–2377. https://doi.org/10.1109/TMECH.2016.2563384

Article  Google Scholar 

Zhao Z, Cai T, Chang F, Cheng X (2019) Real-time surgical instrument detection in robot-assisted surgery using a convolutional neural network cascade. In: Healthcare Technology Letters. pp 275–279

留言 (0)

沒有登入
gif