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Erschienen in: International Journal of Computer Assisted Radiology and Surgery 3/2018

29.01.2018 | Original Article

Video and accelerometer-based motion analysis for automated surgical skills assessment

verfasst von: Aneeq Zia, Yachna Sharma, Vinay Bettadapura, Eric L. Sarin, Irfan Essa

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 3/2018

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Abstract

Purpose

Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS-like surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data).

Methods

We conduct a large study for basic surgical skill assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce “entropy-based” features—approximate entropy and cross-approximate entropy, which quantify the amount of predictability and regularity of fluctuations in time series data. The proposed features are compared to existing methods of Sequential Motion Texture, Discrete Cosine Transform and Discrete Fourier Transform, for surgical skills assessment.

Results

We report average performance of different features across all applicable OSATS-like criteria for suturing and knot-tying tasks. Our analysis shows that the proposed entropy-based features outperform previous state-of-the-art methods using video data, achieving average classification accuracies of 95.1 and 92.2% for suturing and knot tying, respectively. For accelerometer data, our method performs better for suturing achieving 86.8% average accuracy. We also show that fusion of video and acceleration features can improve overall performance for skill assessment.

Conclusion

Automated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.
Literatur
1.
Zurück zum Zitat Martin J, 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(2):273–278CrossRefPubMed Martin J, 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(2):273–278CrossRefPubMed
2.
Zurück zum Zitat Sharma Y, Bettadapura V, Plötz T, Hammerla N, Mellor S, McNaney R, Olivier P, Deshmukh S, McCaskie A, Essa I (2014) Video based assessment of OSATS using sequential motion textures. In: International workshop on modeling and monitoring of computer assisted interventions (M2CAI)—international conference on medical image computing and computer-assisted intervention—MICCAI Sharma Y, Bettadapura V, Plötz T, Hammerla N, Mellor S, McNaney R, Olivier P, Deshmukh S, McCaskie A, Essa I (2014) Video based assessment of OSATS using sequential motion textures. In: International workshop on modeling and monitoring of computer assisted interventions (M2CAI)—international conference on medical image computing and computer-assisted intervention—MICCAI
3.
Zurück zum Zitat Zia A, Sharma Y, Bettadapura V, Sarin EL, Ploetz T, Clements MA, Essa I (2016) Automated video-based assessment of surgical skills for training and evaluation in medical schools. Int J Comput Assist Radiol Surg 11(9):1623–1636CrossRefPubMed Zia A, Sharma Y, Bettadapura V, Sarin EL, Ploetz T, Clements MA, Essa I (2016) Automated video-based assessment of surgical skills for training and evaluation in medical schools. Int J Comput Assist Radiol Surg 11(9):1623–1636CrossRefPubMed
4.
Zurück zum Zitat Bettadapura V, Schindler G, Plötz T, Essa I (2013) Augmenting bag-of-words: data-driven discovery of temporal and structural information for activity recognition. In: CVPR. IEEE Bettadapura V, Schindler G, Plötz T, Essa I (2013) Augmenting bag-of-words: data-driven discovery of temporal and structural information for activity recognition. In: CVPR. IEEE
5.
Zurück zum Zitat Sharma Y, Plötz T, Hammerla N, Mellor S, Roisin M, Olivier P, Deshmukh S, McCaskie A, Essa I (2014) Automated surgical OSATS prediction from videos. In: ISBI. IEEE Sharma Y, Plötz T, Hammerla N, Mellor S, Roisin M, Olivier P, Deshmukh S, McCaskie A, Essa I (2014) Automated surgical OSATS prediction from videos. In: ISBI. IEEE
6.
Zurück zum Zitat Zia A, Sharma Y, Bettadapura V, Sarin EL, Clements MA, Essa I (2015) Automated assessment of surgical skills using frequency analysis. In: International conference on medical image computing and computer-assisted intervention–MICCAI 2015. Springer, pp 430–438 Zia A, Sharma Y, Bettadapura V, Sarin EL, Clements MA, Essa I (2015) Automated assessment of surgical skills using frequency analysis. In: International conference on medical image computing and computer-assisted intervention–MICCAI 2015. Springer, pp 430–438
7.
Zurück zum Zitat Trejos A, Patel R, Naish M, Schlachta C (2008) Design of a sensorized instrument for skills assessment and training in minimally invasive surgery. In: 2nd IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics, 2008. BioRob 2008. IEEE, pp 965–970 Trejos A, Patel R, Naish M, Schlachta C (2008) Design of a sensorized instrument for skills assessment and training in minimally invasive surgery. In: 2nd IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics, 2008. BioRob 2008. IEEE, pp 965–970
8.
Zurück zum Zitat Nisky I, Che Y, Quek ZF, Weber M, Hsieh MH, Okamura AM (2015) Teleoperated versus open needle driving: kinematic analysis of experienced surgeons and novice users. In: 2015 IEEE international conference on robotics and automation (ICRA). IEEE, pp 5371–5377 Nisky I, Che Y, Quek ZF, Weber M, Hsieh MH, Okamura AM (2015) Teleoperated versus open needle driving: kinematic analysis of experienced surgeons and novice users. In: 2015 IEEE international conference on robotics and automation (ICRA). IEEE, pp 5371–5377
9.
Zurück zum Zitat Ershad M, Koesters Z, Rege R, Majewicz A (2016) Meaningful assessment of surgical expertise: semantic labeling with data and crowds. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 508–515 Ershad M, Koesters Z, Rege R, Majewicz A (2016) Meaningful assessment of surgical expertise: semantic labeling with data and crowds. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 508–515
10.
Zurück zum Zitat Brown J, O’Brien C, Leung S, Dumon K, Lee D, Kuchenbecker K (2016) Using contact forces and robot arm accelerations to automatically rate surgeon skill at peg transfer. IEEE Trans Biomed Eng 64:2263–2275CrossRefPubMed Brown J, O’Brien C, Leung S, Dumon K, Lee D, Kuchenbecker K (2016) Using contact forces and robot arm accelerations to automatically rate surgeon skill at peg transfer. IEEE Trans Biomed Eng 64:2263–2275CrossRefPubMed
11.
Zurück zum Zitat Rosen J, Hannaford B, Richards CG, Sinanan MN (2001) Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills. IEEE Trans Biomed Eng 48(5):579–591CrossRefPubMed Rosen J, Hannaford B, Richards CG, Sinanan MN (2001) Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills. IEEE Trans Biomed Eng 48(5):579–591CrossRefPubMed
12.
Zurück zum Zitat Reiley C, Hager G (2009) Decomposition of robotic surgical tasks: an analysis of subtasks and their correlation to skill. In: International conference on medical image computing and computer-assisted intervention–MICCAI Reiley C, Hager G (2009) Decomposition of robotic surgical tasks: an analysis of subtasks and their correlation to skill. In: International conference on medical image computing and computer-assisted intervention–MICCAI
13.
Zurück zum Zitat Haro BB, Zappella L, Vidal R (2012) Surgical gesture classification from video data. In: International conference on medical image computing and computer-assisted intervention—MICCAI 2012. Springer, pp 34–41 Haro BB, Zappella L, Vidal R (2012) Surgical gesture classification from video data. In: International conference on medical image computing and computer-assisted intervention—MICCAI 2012. Springer, pp 34–41
14.
Zurück zum Zitat Zappella L, Béjar B, Hager G, Vidal R (2013) Surgical gesture classification from video and kinematic data. Med Image Anal 17(7):732–745CrossRefPubMed Zappella L, Béjar B, Hager G, Vidal R (2013) Surgical gesture classification from video and kinematic data. Med Image Anal 17(7):732–745CrossRefPubMed
15.
Zurück zum Zitat Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N (2017) Endonet: A deep architecture for recognition tasks on laparoscopic videos. IEEE Trans Med Imaging 36(1):86–97CrossRefPubMed Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N (2017) Endonet: A deep architecture for recognition tasks on laparoscopic videos. IEEE Trans Med Imaging 36(1):86–97CrossRefPubMed
16.
Zurück zum Zitat DiPietro R, Lea C, Malpani A, Ahmidi N, Vedula SS, Lee GI, Lee MR, Hager GD (2016) Recognizing surgical activities with recurrent neural networks. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 551–558 DiPietro R, Lea C, Malpani A, Ahmidi N, Vedula SS, Lee GI, Lee MR, Hager GD (2016) Recognizing surgical activities with recurrent neural networks. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 551–558
17.
Zurück zum Zitat Krishnan S, Garg A, Patil S, Lea C, Hager G, Abbeel P, Goldberg K (2018) Transition state clustering: unsupervised surgical trajectory segmentation for robot learning. In: Robotics research. Springer, pp 91–110 Krishnan S, Garg A, Patil S, Lea C, Hager G, Abbeel P, Goldberg K (2018) Transition state clustering: unsupervised surgical trajectory segmentation for robot learning. In: Robotics research. Springer, pp 91–110
18.
Zurück zum Zitat Zia A, Zhang C, Xiong X, Jarc AM (2017) Temporal clustering of surgical activities in robot-assisted surgery. Int J Comput Assist Radiol Surg 12(7):1171–1178CrossRefPubMedPubMedCentral Zia A, Zhang C, Xiong X, Jarc AM (2017) Temporal clustering of surgical activities in robot-assisted surgery. Int J Comput Assist Radiol Surg 12(7):1171–1178CrossRefPubMedPubMedCentral
19.
Zurück zum Zitat Goh AC, Goldfarb DW, Sander JC, Miles BJ, Dunkin BJ (2012) Global evaluative assessment of robotic skills: validation of a clinical assessment tool to measure robotic surgical skills. J Urol 187(1):247–252CrossRefPubMed Goh AC, Goldfarb DW, Sander JC, Miles BJ, Dunkin BJ (2012) Global evaluative assessment of robotic skills: validation of a clinical assessment tool to measure robotic surgical skills. J Urol 187(1):247–252CrossRefPubMed
20.
Zurück zum Zitat Pirsiavash H, Vondrick C, Torralba A (2014) Assessing the quality of actions. In: European conference on computer vision. Springer, pp 556–571 Pirsiavash H, Vondrick C, Torralba A (2014) Assessing the quality of actions. In: European conference on computer vision. Springer, pp 556–571
21.
Zurück zum Zitat Venkataraman V, Vlachos I, Turaga P (2015) Dynamical regularity for action analysis. In: Proceedings of the British machine vision conference (BMVC), pp 67–1 Venkataraman V, Vlachos I, Turaga P (2015) Dynamical regularity for action analysis. In: Proceedings of the British machine vision conference (BMVC), pp 67–1
22.
Zurück zum Zitat Laptev I (2005) On space-time interest points. Int J Comput Vis 64(2–3):107–123CrossRef Laptev I (2005) On space-time interest points. Int J Comput Vis 64(2–3):107–123CrossRef
23.
Zurück zum Zitat Pudil P, Novovičová J, Kittler J (1994) Floating search methods in feature selection. Pattern Recognit Lett 15(11):1119–1125CrossRef Pudil P, Novovičová J, Kittler J (1994) Floating search methods in feature selection. Pattern Recognit Lett 15(11):1119–1125CrossRef
26.
Zurück zum Zitat Sloetjes H, Wittenburg P (2008) Annotation by category: ELAN and ISO DCR. In: Language resources and evaluation conference—LREC Sloetjes H, Wittenburg P (2008) Annotation by category: ELAN and ISO DCR. In: Language resources and evaluation conference—LREC
27.
Zurück zum Zitat McNemar Q (1947) Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2):153–157CrossRefPubMed McNemar Q (1947) Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2):153–157CrossRefPubMed
28.
Zurück zum Zitat Martínez-Zarzuela M, Gómez C, Pernas FJD, Fernández A, Hornero R (2013) Cross-approximate entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings. Comput Methods Programs Biomed 112:189–199CrossRefPubMed Martínez-Zarzuela M, Gómez C, Pernas FJD, Fernández A, Hornero R (2013) Cross-approximate entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings. Comput Methods Programs Biomed 112:189–199CrossRefPubMed
29.
Zurück zum Zitat Gao Y, Vedula SS, Reiley CE, Ahmidi N, Varadarajan B, Lin HC, Tao L, Zappella L, Béjar B, Yuh DD, Chen CCG, Vidal R, Khundanpur S, Hager GD (2014) JHU-ISI gesture and skill assessment working set (JIGSAWS): a surgical activity dataset for human motion modeling. In: International workshop on modeling and monitoring of computer assisted interventions (M2CAI)—international conference on medical image computing and computer-assisted intervention—MICCAI, vol 3 Gao Y, Vedula SS, Reiley CE, Ahmidi N, Varadarajan B, Lin HC, Tao L, Zappella L, Béjar B, Yuh DD, Chen CCG, Vidal R, Khundanpur S, Hager GD (2014) JHU-ISI gesture and skill assessment working set (JIGSAWS): a surgical activity dataset for human motion modeling. In: International workshop on modeling and monitoring of computer assisted interventions (M2CAI)—international conference on medical image computing and computer-assisted intervention—MICCAI, vol 3
Metadaten
Titel
Video and accelerometer-based motion analysis for automated surgical skills assessment
verfasst von
Aneeq Zia
Yachna Sharma
Vinay Bettadapura
Eric L. Sarin
Irfan Essa
Publikationsdatum
29.01.2018
Verlag
Springer International Publishing
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 3/2018
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1704-z

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