ArticleInternational journal of computer assisted radiology and surgery2014
Reliability of sensor-based real-time workflow recognition in laparoscopic cholecystectomy.
Article in International journal of computer assisted radiology and surgery, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 25 citations in OpenAlex.
- Audio-based event detection in the operating room.International journal of computer assisted radiology and surgery · 2024Article
- Deep learning-based surgical step recognition for laparoscopic right-sided colectomy.Langenbeck's archives of surgery · 2024Article
- Surgical reporting for laparoscopic cholecystectomy based on phase annotation by a convolutional neural network (CNN) and the phenomenon of phase flickering: a proof of concept.International journal of computer assisted radiology and surgery · 2022Article
- Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision.Surgical endoscopy · 2022Article
- A learning robot for cognitive camera control in minimally invasive surgery.Surgical endoscopy · 2021Article
- [Artificial intelligence in general and visceral surgery].Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen · 2020Review
- The intelligent OR: design and validation of a context-aware surgical working environment.International journal of computer assisted radiology and surgery · 2018Article
- Surgical process modeling.Innovative surgical sciences · 2017Review
- [Intelligent operating room suite : From passive medical devices to the self-thinking cognitive surgical assistant].Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen · 2016Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLaparoscopic cholecystectomy is a very common minimally invasive surgical procedure that may be improved by autonomous or cooperative assistance support systems. Model-based surgery with a precise definition of distinct procedural tasks (PT) of the operation was implemented and tested to depict and analyze the process of this procedure.
methodsReliability of real-time workflow recognition in laparoscopic cholecystectomy ([Formula: see text] cases) was evaluated by continuous sensor-based data acquisition. Ten PTs were defined including begin/end preparation calots' triangle, clipping/cutting cystic artery and duct, begin/end gallbladder dissection, begin/end hemostasis, gallbladder removal, and end of operation. Data acquisition was achieved with continuous instrument detection, room/table light status, intra-abdominal pressure, table tilt, irrigation/aspiration volume and coagulation/cutting current application. Two independent observers recorded start and endpoint of each step by analysis of the sensor data. The data were cross-checked with laparoscopic video recordings serving as gold standard for PT identification.
resultsBland-Altman analysis revealed for 95% of cases a difference of annotation results within the limits of agreement ranging from [Formula: see text]309 s (PT 7) to +368 s (PT 5). Laparoscopic video and sensor data matched to a greater or lesser extent within the different procedural tasks. In the majority of cases, the observer results exceeded those obtained from the laparoscopic video. Empirical knowledge was required to detect phase transit.
conclusionsA set of sensors used to monitor laparoscopic cholecystectomy procedures was sufficient to enable expert observers to reliably identify each PT. In the future, computer systems may automate the task identification process provided a more robust data inflow is available.
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