ArticleInternational journal of computer assisted radiology and surgery2018
Video and accelerometer-based motion analysis for automated surgical skills assessment.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 126 citations in OpenAlex.
- Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training.Scientific reports · 2024Trial
- Review
- Cross-Stream and Cross-Channel Attention Networks for Surgical Skill Classification in Open Surgery From Hand Kinematics.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- [Application of artificial intelligence for surgical skill assessment and quality control in minimally invasive surgery: progress, problems and prospect].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Semantic-aware self-supervised learning using progressive sub-action regression for action quality assessment.Scientific reports · 2026Article
- Surgical video-based temporal action analysis algorithm and competency assessment in laparoscopic cholecystectomy: development and exploratory evaluation.Surgical endoscopy · 2026Article
- Review
- Automated surgical action recognition and competency assessment in laparoscopic cholecystectomy: a proof-of-concept study.Surgical endoscopy · 2025Article
- Systematic review of machine learning applications using nonoptical motion tracking in surgery.NPJ digital medicine · 2025Article
- Evaluation of single-stage vision models for pose estimation of surgical instruments.International journal of computer assisted radiology and surgery · 2023Article
- Pilot Analysis of Surgeon Instrument Utilization Signatures Based on Shannon Entropy and Deep Learning for Surgeon Performance Assessment in a Cadaveric Carotid Artery Injury Control Simulation.Operative neurosurgery (Hagerstown, Md.) · 2023Article
- A Deep Learning Approach to Classify Surgical Skill in Microsurgery Using Force Data from a Novel Sensorised Surgical Glove.Sensors (Basel, Switzerland) · 2023Article
- Development and validation of GLVS (Generic Laparoscopic Video Scoring System), a tool for assessment of laparoscopic skills in gynaecology using videotaped procedures: Are edited videos representative of their full-length version?Facts, views & vision in ObGyn · 2023Article
- Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2023Review
- Detection of blood stains using computer vision-based algorithms and their association with postoperative outcomes in thoracoscopic lobectomies.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2022Article
- Video-based assessment of intraoperative surgical skill.International journal of computer assisted radiology and surgery · 2022Article
- Multi-Modal Deep Learning for Assessing Surgeon Technical Skill.Sensors (Basel, Switzerland) · 2022Article
- Machine learning for technical skill assessment in surgery: a systematic review.NPJ digital medicine · 2022Review
- Evaluation of surgical skill using machine learning with optimal wearable sensor locations.PloS one · 2022Article
- Automated recognition of objects and types of forceps in surgical images using deep learning.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeBasic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS-like surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data).
methodsWe conduct a large study for basic surgical skill assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce "entropy-based" features-approximate entropy and cross-approximate entropy, which quantify the amount of predictability and regularity of fluctuations in time series data. The proposed features are compared to existing methods of Sequential Motion Texture, Discrete Cosine Transform and Discrete Fourier Transform, for surgical skills assessment.
resultsWe report average performance of different features across all applicable OSATS-like criteria for suturing and knot-tying tasks. Our analysis shows that the proposed entropy-based features outperform previous state-of-the-art methods using video data, achieving average classification accuracies of 95.1 and 92.2% for suturing and knot tying, respectively. For accelerometer data, our method performs better for suturing achieving 86.8% average accuracy. We also show that fusion of video and acceleration features can improve overall performance for skill assessment.
conclusionAutomated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.
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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.