Recognition of the Surgeon's Motions During Endoscopic Operation by Statistics Based Algorithm and Neural Networks Based ANARX Models

Nõmm, Sven; Petlenkov, Eduard; Vain, Jüri; Belikov, Juri; Miyawaki, Fujio; Yoshimitsu, Kitaro

doi:10.3182/20080706-5-kr-1001.02501

Cited by 16 publications

(11 citation statements)

References 12 publications

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“…information that is modelled) is laid out on the granularity level axis defined in Investigations have concentrated on the activity level, but all granularity levels have been studied. At the highest level, the global procedure has been studied (Bhatia et al, 2007;Xiao et al, 2005), as well as the phases (Ahmadi et al, 2006;James et al, 2007;Katic et al, 2010;Klank et al, 2008;Lalys et al, 2012a;Lo et al, 2003;Nara et al, 2011;Padoy et al, 2007Padoy et al, , 2010Qi et al, 2006;Suzuki et al, 2012, Thiemjarus et al, 2012, the steps Bouarfa et al, 2010;Fischer et al, 2005;Jannin et al, 2003Jannin et al, , 2007Ko et al, 2007;Lemke et al, 2004;Malarme et al, 2010), and the motions Lin et al, 2006;Nomm et al, 2008). Some studies integrated two or more of these granularity levels in their modelling (Burgert et al, 2006;Ibbotson et al, 1999;MacKenzie et al, 2001;Münchenberg et al, 2000;Xiao et al, 2005;Yoshimitsu et al 2010).…”

Section: Granularity Levelmentioning

confidence: 99%

“…We then find all 2D graph representations, which have been used mostly, with hierarchical decompositions, state-transition diagrams and non-oriented graphs. Lastly, at the lower level, simple sequential (Agarwal et al, 2007;Ahmadi et al, 2006;Houliston et al, 2011;James et al, 2007;Klank et al, 2008;Nara et al, 2011;Padoy et al, 2007;Suzuki et al, 2012;Xiao et al, 2005) or non-sequential lists Lin et al, 2006;Nomm et al, 2008) were also used, suggesting a list of words for representing one or many levels of the surgery's granularity (Fig 5).…”

Section: Formalisationmentioning

confidence: 99%

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Surgical process modelling: a review

2013

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Section: Granularity Levelmentioning

confidence: 99%

Section: Formalisationmentioning

confidence: 99%

Surgical process modelling: a review

2013

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“…This leads to the idea that switching points of the same type should be somehow grouped in certain locations (or inside certain convex sets in the operating room [5]). If the surgeon's wrist trajectory passes through such convex set, motion switching can be declared.…”

Section: B Statistic-probabilistic Approachmentioning

confidence: 99%

Supervised training of voting automata for the surgeon's motion recognition during laparoscope surgery

Vain

Nõmm

Anier

et al. 2009

2009 IEEE International Conference on Control and Automation

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Supervised learning of voting automata for the surgeon's right hand motion recognition constitutes the main result reported in the present paper. Within the framework of the project, aiming the design of scrub nurse robot a number of methods for recognizing the current stage of the surgery has been developed. Obviously no one of the methods separately can guarantee hundred percent correct recognition. Therefore, the voting automaton is employed to choose the best result. In this paper main attention is paid to the design of such voting automata using supervised learning techniques.

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“…Surgical process modeling has been used in various applications, such as surgical skills evaluation [5,6], operating room management optimization [7,8], or robotic assistance [9,10]. However, SPMs have rarely been applied for surgical quality assessment.…”

Section: Introductionmentioning

confidence: 99%

Offline identification of surgical deviations in laparoscopic rectopexy

Huaulmé

Jannin

Reche

et al. 2020

Artificial Intelligence in Medicine

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Objective: A median of 14.4% of patient undergone at least one adverse event during surgery and a third of them are preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process. Therefore, it is clear that the automatic identification of adverse events is a major challenge for patient safety. In this paper, we have proposed a method enabling us to identify such deviations. We have focused on identifying surgeons' deviations from standard surgical processes due to surgical events rather than anatomic specificities. This is particularly challenging, given the high variability in typical surgical procedure workflows. Methods:We have introduced a new approach designed to automatically detect and distinguish surgical process deviations based on multi-dimensional non-linear temporal scaling with a hidden semi-Markov model using manual annotation of surgical processes. The approach was then evaluated using crossvalidation.Results: The best results have over 90 % accuracy. Recall and precision were superior at 70 %. We have provided a detailed analysis of the incorrectlydetected observations.Conclusion: Multi-dimensional non-linear temporal scaling with a hidden semi-Markov model provides promising results for detecting deviations. Our error analysis of the incorrectly-detected observations offers different leads in order to further improve our method.Significance: Our method demonstrated the feasibility of automatically detecting surgical deviations that could be implemented for both skill analysis and developing situation awareness-based computer-assisted surgical systems.

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Recognition of the Surgeon's Motions During Endoscopic Operation by Statistics Based Algorithm and Neural Networks Based ANARX Models

Cited by 16 publications

References 12 publications

Surgical process modelling: a review

Surgical process modelling: a review

Supervised training of voting automata for the surgeon's motion recognition during laparoscope surgery

Offline identification of surgical deviations in laparoscopic rectopexy

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