ArticleInternational journal of computer assisted radiology and surgery2017
Online time and resource management based on surgical workflow time series analysis.
Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 28 citations in OpenAlex.
- Multimodal semi-supervised learning for online recognition of multi-granularity surgical workflows.International journal of computer assisted radiology and surgery · 2024Article
- Review
- Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy.Annals of surgery · 2019Article
- Surgical data science: The new knowledge domain.Innovative surgical sciences · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeHospitals' effectiveness and efficiency can be enhanced by automating the resource and time management of the most cost-intensive unit in the hospital: the operating room (OR). The key elements required for the ideal organization of hospital staff and technical resources (such as instruments in the OR) are an exact online forecast of both the surgeon's resource usage and the remaining intervention time.
methodsThis paper presents a novel online approach relying on time series analysis and the application of a linear time-variant system. We calculated the power spectral density and the spectrogram of surgical perspectives (e.g., used instrument) of interest to compare several surgical workflows.
resultsConsidering only the use of the surgeon's right hand during an intervention, we were able to predict the remaining intervention time online with an error of 21 min 45 s ±9 min 59 s for lumbar discectomy. Furthermore, the performance of forecasting of technical resource usage in the next 20 min was calculated for a combination of spectral analysis and the application of a linear time-variant system (sensitivity: 74 %; specificity: 75 %) focusing on just the use of surgeon's instrument in question.
conclusionThe outstanding benefit of these methods is that the automated recording of surgical workflows has minimal impact during interventions since the whole set of surgical perspectives need not be recorded. The resulting predictions can help various stakeholders such as OR staff and hospital technicians. Moreover, reducing resource conflicts could well improve patient care.
Indexed as
Identifiers
What 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.