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Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field

  • Daochang Liu
  • Tingting JiangEmail author
  • Yizhou Wang
  • Rulin Miao
  • Fei Shan
  • Ziyu Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

Surgical skill assessment is important for surgery training and quality control. Prior works on this task largely focus on basic surgical tasks such as suturing and knot tying performed in simulation settings. In contrast, surgical skill assessment is studied in this paper on a real clinical dataset, which consists of fifty-seven in-vivo laparoscopic surgeries and corresponding skill scores annotated by six surgeons. From analyses on this dataset, the clearness of operating field (COF) is identified as a good proxy for overall surgical skills, given its strong correlation with overall skills and high inter-annotator consistency. Then an objective and automated framework based on neural network is proposed to predict surgical skills through the proxy of COF. The neural network is jointly trained with a supervised regression loss and an unsupervised rank loss. In experiments, the proposed method achieves 0.55 Spearman’s correlation with the ground truth of overall technical skill, which is even comparable with the human performance of junior surgeons.

Keywords

Surgical skill assessment Clinical data Neural networks 

Notes

Acknowledgement

This work was partially supported by National Basic Research Program of China (973 Program) under contract 2015CB351803, the Natural Science Foundation of China under contracts 61572042, 61527804 and 61625201. We also acknowledge the Clinical Medicine Plus X-Young Scholars Project and High-Performance Computing Platform of Peking University.

Supplementary material

490279_1_En_53_MOESM1_ESM.zip (59.8 mb)
Supplementary material 1 (zip 61186 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Daochang Liu
    • 1
  • Tingting Jiang
    • 1
    Email author
  • Yizhou Wang
    • 1
    • 3
    • 4
  • Rulin Miao
    • 2
  • Fei Shan
    • 2
  • Ziyu Li
    • 2
  1. 1.NELVT, Department of Computer SciencePeking UniversityBeijingChina
  2. 2.Peking University Cancer HospitalBeijingChina
  3. 3.Peng Cheng LabShenzhenChina
  4. 4.Deepwise AI LabBeijingChina

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