ArticleInternational journal of computer assisted radiology and surgery2024
Automatic performance evaluation of the intracorporeal suture exercise.
Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic Surgery.Journal of personalized medicine · 2025Review
- Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review.Journal of medical Internet research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis work uses deep learning algorithms to provide automated feedback on the suture with intracorporeal knot exercise in the fundamentals of laparoscopic surgery simulator. Different metrics were designed to provide informative feedback to the user on how to complete the task more efficiently. The automation of the feedback will allow students to practice at any time without the supervision of experts.
methodsFive residents and five senior surgeons participated in the study. Object detection, image classification, and semantic segmentation deep learning algorithms were used to collect statistics on the practitioner's performance. Three task-specific metrics were defined. The metrics refer to the way the practitioner holds the needle before the insertion to the Penrose drain, and the amount of movement of the Penrose drain during the needle's insertion.
resultsGood agreement between the human labeling and the different algorithms' performance and metric values was achieved. The difference between the scores of the senior surgeons and the surgical residents was statistically significant for one of the metrics.
conclusionWe developed a system that provides performance metrics of the intracorporeal suture exercise. These metrics can help surgical residents practice independently and receive informative feedback on how they entered the needle into the Penrose.
Indexed as
Identifiers
37278834What OpenQuestion holds
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.