Evidence map›Paper›PMID 30779023›Full record

ArticleInternational journal of computer assisted radiology and surgery2019

Automatic and near real-time stylistic behavior assessment in robotic surgery.

M Ershad, R Rege, Ann Majewicz Fey

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
4.9field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

  1. Pooled it
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  7. Machine learning in healthcare citizen science: A scoping review.International journal of medical informatics · 2025
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  12. Cannulation Skill Assessment Using Functional Data Analysis.IEEE journal of biomedical and health informatics · 2023
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  14. Review
  15. Article
  16. Article
  17. An explainable machine learning method for assessing surgical skill in liposuction surgery.International journal of computer assisted radiology and surgery · 2022
    Article
  18. Toward Correcting Anxious Movements Using Haptic Cues on the Da Vinci Surgical Robot.Proceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

M ErshadDepartment of Electrical Engineering, University of Texas at Dallas, Richardson, TX, 75080, USA. marzieh.ershadlangroodi@utdallas.edu.ORCID http://orcid.org/0000-0002-0579-1409
R RegeDepartment of Surgery, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
Ann Majewicz FeyDepartment of Surgery, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
The University of Texas at Dallas · USSouthwestern Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAutomatic skill evaluation is of great importance in surgical robotic training. Extensive research has been done to evaluate surgical skill, and a variety of quantitative metrics have been proposed. However, these methods primarily use expert selected features which may not capture latent information in movement data. In addition, these features are calculated over the entire task time and are provided to the user after the completion of the task. Thus, these quantitative metrics do not provide users with information on how to modify their movements to improve performance in real time. This study focuses on automatic stylistic behavior recognition that has the potential to be implemented in near real time.

methodsWe propose a sparse coding framework for automatic stylistic behavior recognition in short time intervals using only position data from the hands, wrist, elbow, and shoulder. A codebook is built for each stylistic adjective using the positive and negative labels provided for each trial through crowd sourcing. Sparse code coefficients are obtained for short time intervals (0.25 s) in a trial using this codebook. A support vector machine classifier is trained and validated through tenfold cross-validation using the sparse codes from the training set.

resultsThe results indicate that the proposed dictionary learning method is able to assess stylistic behavior performance in near real time using user joint position data with improved accuracy compared to using PCA features or raw data.

conclusionThe possibility to automatically evaluate a trainee's style of movement in short time intervals could provide the user with online customized feedback and thus improve performance during surgical tasks.

Indexed as

Clinical CompetenceFeedbackCrowdsourcingHumansRobotic Surgical ProceduresCrowdsourcingRobotic surgerySurgical skill assessment

Identifiers

PMID30779023
OpenAlexW2916247357

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.