Abstract
Purpose
Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees’ surgical skills in order to improve surgical practice.
Methods
In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees’ motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression.
Results
Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs’ efficiency, we were able to minimize its black-box effect using the class activation map technique.
Conclusions
This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within “Operation Room 2.0” and support novice surgeons in improving their skills to eventually become experts.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
Our source code will be publicly available upon the acceptance of the paper.
References
Ahmidi N, Tao L, Sefati S, Gao Y, Lea C, Haro BB, Zappella L, Khudanpur S, Vidal R, Hager GD (2017) A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery. IEEE Trans Biomed Eng 64(9):2025–2041
Bridgewater B, Grayson AD, Jackson M, Brooks N, Grotte GJ, Keenan DJ, Millner R, Fabri BM, Mark J (2003) Surgeon specific mortality in adult cardiac surgery: comparison between crude and risk stratified data. BMJ 327(7405):13–17
Chollet Fea (2015) Keras. https://keras.io
Forestier G, Petitjean F, Senin P, Despinoy F, Jannin P (2017) Discovering discriminative and interpretable patterns for surgical motion analysis. In: Artificial intelligence in medicine, pp 136–145
Forestier G, Petitjean F, Senin P, Despinoy F, Huaulmé A, Ismail Fawaz H, Weber J, Idoumghar L, Muller PA, Jannin P (2018) Surgical motion analysis using discriminative interpretable patterns. Artif Intell Med 91:3–11
Gao Y, Vedula SS, Reiley CE, Ahmidi N, Varadarajan B, Lin HC, Tao L, Zappella L, Béjar B, Yuh DD, Chen CCG, Vidal R, Khudanpur S, Hager GD (2014) The JHU-ISI gesture and skill assessment working set (JIGSAWS): a surgical activity dataset for human motion modeling. In: Modeling and monitoring of computer assisted interventions—MICCAI workshop
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Int Conf Artif Intell Stat 9:249–256
Hatala R, Cook DA, Brydges R, Hawkins R (2015) Constructing a validity argument for the objective structured assessment of technical skills (OSATS): a systematic review of validity evidence. Adv Health Sci Educ 20(5):1149–1175
Intuitive Surgical Sunnyvale CA (2018) The Da Vinci Surgical System
Islam G, Kahol K, Li B, Smith M, Patel VL (2016) Affordable, web-based surgical skill training and evaluation tool. J Biomed Inf 59:102–114
Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2018) Evaluating surgical skills from kinematic data using convolutional neural networks. In: International conference on medical image computing and computer assisted intervention, pp 214–221
Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2018) Transfer learning for time series classification. In: IEEE international conference on big data, pp 1367–1376
Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2019) Deep learning for time series classification: a review. Data Mining and Knowledge Discovery
Kassahun Y, Yu B, Tibebu AT, Stoyanov D, Giannarou S, Metzen JH, Vander Poorten E (2016) Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions. Int J Comput Assist Radiol Surg 11(4):553–568
Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: International conference on learning representations
Maier-Hein L, Vedula SS, Speidel S, Navab N, Kikinis R, Park A, Eisenmann M, Feussner H, Forestier G, Giannarou S, Hashizume M, Katic D, Kenngott H, Kranzfelder M, Malpani A, März K, Neumuth T, Padoy N, Pugh C, Schoch N, Stoyanov D, Taylor R, Wagner M, Hager GD, Jannin P (2017) Surgical data science for next-generation interventions. Nat Biomed Eng 1(9):691–696
Niitsu H, Hirabayashi N, Yoshimitsu M, Mimura T, Taomoto J, Sugiyama Y, Murakami S, Saeki S, Mukaida H, Takiyama W (2013) Using the objective structured assessment of technical skills (OSATS) global rating scale to evaluate the skills of surgical trainees in the operating room. Surg Today 43(3):271–275
Polavarapu HV, Kulaylat A, Sun S, Hamed O (2013) 100 years of surgical education: the past, present, and future. Bull Am Coll Surg 98(7):22–29
Tao L, Elhamifar E, Khudanpur S, Hager GD, Vidal R (2012) Sparse hidden Markov models for surgical gesture classification and skill evaluation. In: Information processing in computer-assisted interventions, pp 167–177
Vedula SS, Malpani AO, Tao L, Chen G, Gao Y, Poddar P, Ahmidi N, Paxton C, Vidal R, Khudanpur S, Hager GD, Chen CCG (2016) Analysis of the structure of surgical activity for a suturing and knot-tying task. Public Libr Sci One 11(3):1–14
Wang Z, Majewicz Fey A (2018) Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. Int J Comput Assist Radiol Surg 13(12):1959–1970
Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: International joint conference on neural networks, pp 1578–1585
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: IEEE conference on computer vision and pattern recognition, pp 2921–2929
Zia A, Essa I (2018) Automated surgical skill assessment in RMIS training. Int J Comput Assist Radiol Surg 13(5):731–739
Acknowledgements
The authors would like to thank the creators of JIGSAWS, as well as NVIDIA Corporation for the GPU grant and the Mésocentre of Strasbourg for providing access to the cluster. The authors would also like to thank the MICCAI 2018 anonymous reviewers for their fruitful comments that helped us improve the quality of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ismail Fawaz, H., Forestier, G., Weber, J. et al. Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks. Int J CARS 14, 1611–1617 (2019). https://doi.org/10.1007/s11548-019-02039-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-019-02039-4