Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions.

By Henry C. Lin, Izhak Shafran, D. Yuh, Gregory Hager. 2006

81 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Gregory Hager
2. Carol E. Reiley
3. Jonathan Krause
4. Erion Plaku
5. Serena Yeung
6. Li Fei-Fei

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Li Fei-Fei
3. Trevor Darrell
4. Andrew Zisserman
5. Xiaogang Wang
6. Dacheng Tao
7. Jitendra Malik
8. Kristen Grauman

In Surgery

1. Gregory Hager
2. Carol E. Reiley
3. Erion Plaku

Citations in the corpus

• Motion generation of robotic surgical tasks: Learning from expert demonstrations (2010, methodology): “Our approach begins with extracting surgemes from the raw motion data from expert trials [ 8 ].”
• Adverse Events in Robotic Surgery: A Retrospective Study of 14 Years of FDA Data (2015, unknown): “Common Surgery Types Hysterectomy (2,331) Prostatectomy (1,291) Thoracic (110) Thyroidectomy (19) Cholecystectomy (118) Hernia repair (37) Myomectomy (328) Nephrectomy (138) Lobectomy (67) Tongue base resection (19) Colectomy (61) Nissen fundoplication (34) Sacrocolpopexy (170) Pyeloplasty (31) Mitr”
• Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks (2018, methodology): “s of a Bayes classifier in differentiating the skill level of intermediate and expert surgeons on a suturing task, processing features extracted from motion data output by the da Vinci surgical system [23]. Additionally, Reiley et al. use Hidden Markov Models for the task and gesture levels to assess”