scholarly journals Data Warehousing Technology for Surgical Workflow Analysis

2008 ◽  
Author(s):  
Thomas Neumuth ◽  
Svetlana Mansmann ◽  
Marc H. Scholl ◽  
Oliver Burgert
Keyword(s):  
Data Warehousing ◽  
Surgical Workflow ◽  
Surgical Workflow Analysis ◽  
Workflow Analysis

Surgical Workflow Analysis: Identifying User Requirements for Surgical Information Systems

2007 ◽  
pp. 229-241 ◽  
Author(s):  
A. Jalote-Parmar ◽  
P.M.T. Pattynama ◽  
H. de Ridder ◽  
R.H.M. Goossens ◽  
A. Freudenthal ◽  
...  
Keyword(s):  
Information Systems ◽  
User Requirements ◽  
Surgical Workflow ◽  
Surgical Workflow Analysis ◽  
Workflow Analysis

scholarly journals Surgical Workflow Analysis in Cerebral Aneurysm Coiling

2020 ◽  
Vol 34 (S1) ◽  
pp. 1-1
Author(s):  
Oleksiy Zaika ◽  
Mel Boulton ◽  
Roy Eagleson ◽  
Sandrine de Ribaupierre
Keyword(s):  
Cerebral Aneurysm ◽  
Surgical Workflow ◽  
Surgical Workflow Analysis ◽  
Workflow Analysis ◽  
Aneurysm Coiling

scholarly journals Active learning using deep Bayesian networks for surgical workflow analysis

2019 ◽  
Vol 14 (6) ◽  
pp. 1079-1087 ◽  
Author(s):  
Sebastian Bodenstedt ◽  
Dominik Rivoir ◽  
Alexander Jenke ◽  
Martin Wagner ◽  
Michael Breucha ◽  
...  
Keyword(s):  
Active Learning ◽  
Bayesian Networks ◽  
Surgical Workflow ◽  
Surgical Workflow Analysis ◽  
Workflow Analysis

Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0)

2021 ◽  
pp. 1-8
Author(s):  
Danyal Z. Khan ◽  
Imanol Luengo ◽  
Santiago Barbarisi ◽  
Carole Addis ◽  
Lucy Culshaw ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Study ◽  
Time Integration ◽  
Surgical Techniques ◽  
Pituitary Surgery ◽  
Surgical Workflow ◽  
Surgical Workflow Analysis ◽  
Workflow Analysis ◽  
Long Term Study ◽  
Tumor Identification

OBJECTIVE Surgical workflow analysis involves systematically breaking down operations into key phases and steps. Automatic analysis of this workflow has potential uses for surgical training, preoperative planning, and outcome prediction. Recent advances in machine learning (ML) and computer vision have allowed accurate automated workflow analysis of operative videos. In this Idea, Development, Exploration, Assessment, Long-term study (IDEAL) stage 0 study, the authors sought to use Touch Surgery for the development and validation of an ML-powered analysis of phases and steps in the endoscopic transsphenoidal approach (eTSA) for pituitary adenoma resection, a first for neurosurgery. METHODS The surgical phases and steps of 50 anonymized eTSA operative videos were labeled by expert surgeons. Forty videos were used to train a combined convolutional and recurrent neural network model by Touch Surgery. Ten videos were used for model evaluation (accuracy, F1 score), comparing the phase and step recognition of surgeons to the automatic detection of the ML model. RESULTS The longest phase was the sellar phase (median 28 minutes), followed by the nasal phase (median 22 minutes) and the closure phase (median 14 minutes). The longest steps were step 5 (tumor identification and excision, median 17 minutes); step 3 (posterior septectomy and removal of sphenoid septations, median 14 minutes); and step 4 (anterior sellar wall removal, median 10 minutes). There were substantial variations within the recorded procedures in terms of video appearances, step duration, and step order, with only 50% of videos containing all 7 steps performed sequentially in numerical order. Despite this, the model was able to output accurate recognition of surgical phases (91% accuracy, 90% F1 score) and steps (76% accuracy, 75% F1 score). CONCLUSIONS In this IDEAL stage 0 study, ML techniques have been developed to automatically analyze operative videos of eTSA pituitary surgery. This technology has previously been shown to be acceptable to neurosurgical teams and patients. ML-based surgical workflow analysis has numerous potential uses—such as education (e.g., automatic indexing of contemporary operative videos for teaching), improved operative efficiency (e.g., orchestrating the entire surgical team to a common workflow), and improved patient outcomes (e.g., comparison of surgical techniques or early detection of adverse events). Future directions include the real-time integration of Touch Surgery into the live operative environment as an IDEAL stage 1 (first-in-human) study, and further development of underpinning ML models using larger data sets.

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