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PhD Thesis (2014) by Yachna Sharma “Surgical Skill Assessment Using Motion Texture analysis”

May 2nd, 2014 Irfan Essa Posted in Medical, PhD, Yachna Sharma No Comments »

Thesis title: Surgical Skill Assessment Using Motion Texture analysis

Yachna Sharma, Ph. D. Candidate, ECE


Prof. Irfan Essa (advisor), College of Computing
Prof. Mark A. Clements (co-advisor), School of Electrical and Computer Engineering
Prof. David Anderson, School of Electrical and Computer Engineering
Prof. Anthony Yezzi, School of Electrical and Computer Engineering
Prof. Christopher F. Barnes, School of Electrical and Computer Engineering
Dr. Thomas Ploetz, Culture lab, School of Computing Science, Newcastle University, United Kingdom
Dr. Eric L. Sarin, Division of Cardiothoracic Surgery, Department of Surgery, Emory University School of Medicine


The objective of this Ph.D. research is to design and develop a framework for automated assessment of surgical skills.Automated assessment can help expedite the manual assessment process and provide unbiased evaluations with possible dexterity feedback.

Evaluation of surgical skills is an important aspect in training of medical students. Current practices rely on manual evaluations from faculty and residents and are time consuming. Proposed solutions in literature involve retrospective evaluations such as watching the offline videos. It requires precious time and attention of expert surgeons and may vary from one surgeon to another. With recent advancements in computer vision and machine learning techniques, the retrospective video evaluation can be best delegated to the computer algorithms.

Skill assessment is a challenging task requiring expert domain knowledge that may be difficult to translate into algorithms. To emulate this human observation process, an appropriate data collection mechanism is required to track motion of the surgeon’s hand in an unrestricted manner. In addition, it is essential to identify skill defining motion dynamics and skill relevant hand locations.

This Ph.D. research aims to address the limitations of manual skill assessment by developing an automated motion analysis framework. Specifically, we propose (1) to design and implement quantitative features to capture fine motion details from surgical video data, (2) to identify and test the efficacy of a core subset of features in classifying the surgical students into different expertise levels, (3) to derive absolute skill scores using regression methods and (4) to perform dexterity analysis using motion data from different hand locations.

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Paper in IBSI 2014 conference entitled “Automated Surgical OSATS Prediction from Videos”

April 28th, 2014 Irfan Essa Posted in Behavioral Imaging, Health Systems, Medical, Papers, Thomas Ploetz, Yachna Sharma No Comments »

  • Y. Sharma, T. Ploetz, N. Hammerla, S. Mellor, R. McNaney, P. Oliver, S. Deshmukh, A. McCaskie, and I. Essa (2014), “Automated Surgical OSATS Prediction from Videos,” in Proceedings of IEEE International Symposium on Biomedical Imaging, Beijing, CHINA, 2014. [PDF] [BIBTEX]
      Address = {Beijing, CHINA},
      Author = {Yachna Sharma and Thomas Ploetz and Nils Hammerla and Sebastian Mellor and Roisin McNaney and Patrick Oliver and Sandeep Deshmukh and Andrew McCaskie and Irfan Essa},
      Booktitle = {{Proceedings of IEEE International Symposium on Biomedical Imaging}},
      Date-Added = {2014-04-28 16:51:07 +0000},
      Date-Modified = {2014-04-28 17:07:29 +0000},
      Month = {April},
      Pdf = {},
      Title = {Automated Surgical {OSATS} Prediction from Videos},
      Year = {2014}}


The assessment of surgical skills is an essential part of medical training. The prevalent manual evaluations by expert surgeons are time consuming and often their outcomes vary substantially from one observer to another. We present a video-based framework for automated evaluation of surgical skills based on the Objective Structured Assessment of Technical Skills (OSATS) criteria. We encode the motion dynamics via frame kernel matrices, and represent the motion granularity by texture features. Linear discriminant analysis is used to derive a reduced dimensionality feature space followed by linear regression to predict OSATS skill scores. We achieve statistically significant correlation (p-value < 0.01) between the ground-truth (given by domain experts) and the OSATS scores predicted by our framework.

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