ArticleInternational journal of computer assisted radiology and surgery2019
A computer vision technique for automated assessment of surgical performance using surgeons' console-feed videos.
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 32 papers, 5 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Artificial intelligence in urological education: a systematic review.BMC medical education · 2026Pooled it
- Artificial intelligence assisted 3D in the robotic urooncology? A systematic review and narrative synthesis of current applications, challenges and future directions.The Canadian journal of urology · 2026Pooled it
- Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review.The British journal of surgery · 2024Pooled it
- Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.Surgical endoscopy · 2023Pooled it
- Evolving robotic surgery training and improving patient safety, with the integration of novel technologies.World journal of urology · 2021Pooled it
- Artificial Intelligence-Based Video Feedback to Improve Novice Performance on Robotic Suturing Skills: A Pilot Study.Journal of endourology · 2024Trial
- Artificial intelligence in robotic urologic surgery: a scoping review.World journal of urology · 2026Article
- Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
- Advancing Genitourinary Cancer Surgery: The Role of Artificial Intelligence and Robotics.Journal of clinical medicine · 2026Review
- Automated Operative Phase and Step Recognition in Vestibular Schwannoma Surgery: Development and Preclinical Evaluation of a Deep Learning Neural Network (IDEAL Stage 0).Neurosurgery · 2026Article
- Deep learning-based vessel and nerve recognition model for lateral lymph node dissection: a retrospective feasibility study.Langenbeck's archives of surgery · 2025Article
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Artificial Intelligence in Orthopaedic and Trauma Surgery Education: Applications, Ethics, and Future Perspectives.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025Review
- Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.BJS open · 2025Article
- Computer vision for evaluating retraction of the neurovascular bundle during nerve-sparing prostatectomy.Journal of robotic surgery · 2025Article
- Artificial intelligence in robot-assisted radical prostatectomy: where do we stand today?Journal of robotic surgery · 2024Review
- Article
- The Contemporary Role of Salvage Radical Prostatectomy in the Management of Recurrent Prostate Cancer: An Up-to-Date Review.Life (Basel, Switzerland) · 2024Review
- Review
- 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
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate an automated assessment of surgical performance (AASP) system for objective and computerized assessment of pelvic lymph node dissection (PLND) as an integral part of robot-assisted radical cystectomy (RARC) using console-feed videos recorded during live surgery.
methodsVideo recordings of 20 PLNDs were included. The quality of lymph node clearance was assessed based on the features derived from the computer vision process which include: the number and cleared area of the vessels/nerve (N-Vs); image median color map; and mean entropy (measures the level of disorganization) in the video frame. The automated scores were compared to the validated pelvic lymphadenectomy appropriateness and completion evaluation (PLACE) scoring rated by a panel of expert surgeons. Logistic regression analysis was employed to compare automated scores versus PLACE scores.
resultsFourteen procedures were used to develop the AASP algorithm. A logistic regression model was trained and validated using the aforementioned features with 30% holdout cross-validation. The model was tested on the remaining six procedures, and the accuracy of predicting the expert-based PLACE scores was 83.3%.
conclusionsTo our knowledge, this is the first automated surgical skill assessment tool that provides an objective evaluation of surgical performance with high accuracy compared to expert surgeons' assessment that can be extended to any endoscopic or robotic video-enabled surgical procedure.
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Registered trials
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