ArticleSurgical endoscopy2023
Acquisition and usage of robotic surgical data for machine learning analysis.
Article in Surgical endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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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.
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Who cites it
16 citing papers in PubMed, 16 citations in OpenAlex.
- Artificial intelligence-based action recognition and skill assessment in robotic cardiac surgery simulation: a feasibility study.Journal of robotic surgery · 2025Trial
- Beyond robotic platforms: artificial intelligence and the emergence of intelligent surgical ecosystems in colorectal surgery.Journal of robotic surgery · 2026Review
- Artificial intelligence in robotic urologic surgery: a scoping review.World journal of urology · 2026Article
- MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- Artificial intelligence-driven real-time assistance in minimally invasive surgery: a technology-oriented narrative review.Journal of minimally invasive surgery · 2026Review
- Machine Learning Models for Objective Assessment of Vascular Anastomoses Using Computational Fluid Dynamics for Surgical Skill Training-A Retrospective Study.Journal of clinical medicine · 2026Article
- Artificial intelligence in healthcare, dentistry, and orthodontics; is it an opportunity or a threat? An epistemological review.Philosophy, ethics, and humanities in medicine : PEHM · 2026Review
- Decoding the OR Black Box and Similar Technologies and Considerations in Otolaryngologic Surgery.Laryngoscope investigative otolaryngology · 2026Review
- Intraoperative robot telemetry and postoperative outcomes in robotic urologic surgery: a single-surgeon retrospective cohort study.Journal of robotic surgery · 2026Article
- Closing the data gap: leveraging pretrained neural networks for robotic surgical assessment on limited clinical data.Journal of robotic surgery · 2025Article
- The rise of robotics: Surgical approaches for rectal cancer over time.Surgical endoscopy · 2025Article
- Robotics in arthroplasty: how good are they?JSES international · 2025Review
- Video-based robotic surgical action recognition and skills assessment on porcine models using deep learning.Surgical endoscopy · 2025Article
- Robotic performance metrics model fellow proficiency in living donor nephrectomy.Journal of robotic surgery · 2024Article
- Revolutionary Advances of Robotic Surgery in Urology Field.Juntendo Iji zasshi = Juntendo medical journal · 2024Review
- Teachers should apply the principle of reduction for more sustainable surgical simulation practice: the example of training pharyngolaryngeal surgery in a porcine model.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe increasing use of robot-assisted surgery (RAS) has led to the need for new methods of assessing whether new surgeons are qualified to perform RAS, without the resource-demanding process of having expert surgeons do the assessment. Computer-based automation and artificial intelligence (AI) are seen as promising alternatives to expert-based surgical assessment. However, no standard protocols or methods for preparing data and implementing AI are available for clinicians. This may be among the reasons for the impediment to the use of AI in the clinical setting.
methodWe tested our method on porcine models with both the da Vinci Si and the da Vinci Xi. We sought to capture raw video data from the surgical robots and 3D movement data from the surgeons and prepared the data for the use in AI by a structured guide to acquire and prepare video data using the following steps: 'Capturing image data from the surgical robot', 'Extracting event data', 'Capturing movement data of the surgeon', 'Annotation of image data'.
results15 participant (11 novices and 4 experienced) performed 10 different intraabdominal RAS procedures. Using this method we captured 188 videos (94 from the surgical robot, and 94 corresponding movement videos of the surgeons' arms and hands). Event data, movement data, and labels were extracted from the raw material and prepared for use in AI.
conclusionWith our described methods, we could collect, prepare, and annotate images, events, and motion data from surgical robotic systems in preparation for its use in AI.
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Registered trials
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.