ArticleInternational journal of computer assisted radiology and surgery2019
"Deep-Onto" network for surgical workflow and context recognition.
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 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Untangling surgical gesture analysis-are we even speaking the same language? a systematic review.Surgical endoscopy · 2025Pooled it
- Artificial intelligence in robotic urologic surgery: a scoping review.World journal of urology · 2026Article
- Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.BJS open · 2025Article
- Semantic segmentation dataset authoring with simplified labels.International journal of computer assisted radiology and surgery · 2025Article
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Clinical applications of artificial intelligence in robotic urologic surgery.Asian journal of urology · 2025Article
- Artificial Intelligence-Augmented Advancements in the Diagnostic Challenges Within Renal Cell Carcinoma.Journal of clinical medicine · 2025Review
- An analysis on the effect of body tissues and surgical tools on workflow recognition in first person surgical videos.International journal of computer assisted radiology and surgery · 2024Article
- Recent Advances in Robotic Surgery for Urologic Tumors.Medicina (Kaunas, Lithuania) · 2024Review
- Automated performance metrics and surgical gestures: two methods for assessment of technical skills in robotic surgery.Journal of robotic surgery · 2024Article
- Evaluation of single-stage vision models for pose estimation of surgical instruments.International journal of computer assisted radiology and surgery · 2023Article
- Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Surgical workflow recognition with temporal convolution and transformer for action segmentation.International journal of computer assisted radiology and surgery · 2023Article
- Automatic Assessment of Procedural Skills Based on the Surgical Workflow Analysis Derived from Speech and Video.Bioengineering (Basel, Switzerland) · 2022Article
- Artificial intelligence for renal cancer: From imaging to histology and beyond.Asian journal of urology · 2022Review
- Concepts and Trends n Autonomy for Robot-Assisted Surgery.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2022Article
- New imaging technologies for robotic kidney cancer surgery.Asian journal of urology · 2022Review
- State-of-the-art of situation recognition systems for intraoperative procedures.Medical & biological engineering & computing · 2022Review
- Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision.Surgical endoscopy · 2022Article
- Surgical workflow recognition with 3DCNN for Sleeve Gastrectomy.International journal of computer assisted radiology and surgery · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeSurgical workflow recognition and context-aware systems could allow better decision making and surgical planning by providing the focused information, which may eventually enhance surgical outcomes. While current developments in computer-assisted surgical systems are mostly focused on recognizing surgical phases, they lack recognition of surgical workflow sequence and other contextual element, e.g., "Instruments." Our study proposes a hybrid approach, i.e., using deep learning and knowledge representation, to facilitate recognition of the surgical workflow.
methodsWe implemented "Deep-Onto" network, which is an ensemble of deep learning models and knowledge management tools, ontology and production rules. As a prototypical scenario, we chose robot-assisted partial nephrectomy (RAPN). We annotated RAPN videos with surgical entities, e.g., "Step" and so forth. We performed different experiments, including the inter-subject variability, to recognize surgical steps. The corresponding subsequent steps along with other surgical contexts, i.e., "Actions," "Phase" and "Instruments," were also recognized.
resultsThe system was able to recognize 10 RAPN steps with the prevalence-weighted macro-average (PWMA) recall of 0.83, PWMA precision of 0.74, PWMA F1 score of 0.76, and the accuracy of 74.29% on 9 videos of RAPN.
conclusionWe found that the combined use of deep learning and knowledge representation techniques is a promising approach for the multi-level recognition of RAPN surgical workflow.
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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.