ArticleInternational journal of computer assisted radiology and surgery2019
Novel evaluation of surgical activity recognition models using task-based efficiency metrics.
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 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 6 citations in OpenAlex.
- Review of automated performance metrics to assess surgical technical skills in robot-assisted laparoscopy.Surgical endoscopy · 2022Pooled it
- Evolving robotic surgery training and improving patient safety, with the integration of novel technologies.World journal of urology · 2021Pooled it
- 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
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Systematic review of machine learning applications using nonoptical motion tracking in surgery.NPJ digital medicine · 2025Article
- Predicting task performance in robot-assisted surgery using physiological stress and subjective workload: a case study with interpretable machine learning.Frontiers in human neuroscience · 2025Article
- Artificial intelligence integration in surgery through hand and instrument tracking: a systematic literature review.Frontiers in surgery · 2025Review
- Automated task-level autonomy assessment in robotic surgery.Surgical endoscopy · 2024Article
- Recent Advances in Robotic Surgery for Urologic Tumors.Medicina (Kaunas, Lithuania) · 2024Review
- SDA-CLIP: surgical visual domain adaptation using video and text labels.Quantitative imaging in medicine and surgery · 2023Article
- Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.Current urology reports · 2023Review
- State-of-the-art of situation recognition systems for intraoperative procedures.Medical & biological engineering & computing · 2022Review
- The role of AI technology in prediction, diagnosis and treatment of colorectal cancer.Artificial intelligence review · 2022Article
- Identification of Main Influencers of Surgical Efficiency and Variability Using Task-Level Objective Metrics: A Five-Year Robotic Sleeve Gastrectomy Case Series.Frontiers in surgery · 2022Article
- How to Bring Surgery to the Next Level: Interpretable Skills Assessment in Robotic-Assisted Surgery.Visceral medicine · 2020Article
- Machine learning in the optimization of robotics in the operative field.Current opinion in urology · 2020Review
- Performance evaluation in cataract surgery with an ensemble of 2D-3D convolutional neural networks.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSurgical task-based metrics (rather than entire procedure metrics) can be used to improve surgeon training and, ultimately, patient care through focused training interventions. Machine learning models to automatically recognize individual tasks or activities are needed to overcome the otherwise manual effort of video review. Traditionally, these models have been evaluated using frame-level accuracy. Here, we propose evaluating surgical activity recognition models by their effect on task-based efficiency metrics. In this way, we can determine when models have achieved adequate performance for providing surgeon feedback via metrics from individual tasks.
methodsWe propose a new CNN-LSTM model, RP-Net-V2, to recognize the 12 steps of robotic-assisted radical prostatectomies (RARP). We evaluated our model both in terms of conventional methods (e.g., Jaccard Index, task boundary accuracy) as well as novel ways, such as the accuracy of efficiency metrics computed from instrument movements and system events.
resultsOur proposed model achieves a Jaccard Index of 0.85 thereby outperforming previous models on RARP. Additionally, we show that metrics computed from tasks automatically identified using RP-Net-V2 correlate well with metrics from tasks labeled by clinical experts.
conclusionWe demonstrate that metrics-based evaluation of surgical activity recognition models is a viable approach to determine when models can be used to quantify surgical efficiencies. We believe this approach and our results illustrate the potential for fully automated, postoperative efficiency reports.
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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.