Making large-scale support vector machine learning practical
Advances in kernel methods
Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
On-line recognition of surgical activity for monitoring in the operating room
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Automatic phases recognition in pituitary surgeries by microscope images classification
IPCAI'10 Proceedings of the First international conference on Information processing in computer-assisted interventions
Modeling and segmentation of surgical workflow from laparoscopic video
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Surgical phases detection from microscope videos by combining SVM and HMM
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
An application-dependent framework for the recognition of high-level surgical tasks in the OR
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Classification of surgical processes using dynamic time warping
Journal of Biomedical Informatics
Surgical workflow monitoring based on trajectory data mining
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
An eye-hand data fusion framework for pervasive sensing of surgical activities
Pattern Recognition
Intervention time prediction from surgical low-level tasks
Journal of Biomedical Informatics
Multi-site study of surgical practice in neurosurgery based on surgical process models
Journal of Biomedical Informatics
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Managers of operating rooms (ORs) and of units upstream (e.g., ambulatory surgery) and downstream (e.g., intensive care and post-anesthesia care) of the OR require real-time information about OR occupancy. Which ORs are in use, and when will each ongoing operation end? This information is used to make decisions about how to assign staff, when to prepare patients for the OR, when to schedule add-on cases, when to move cases, and how to prioritize room cleanups (Dexter et at. 2004). It is typically gathered by OR managers manually, by walking to each OR and estimating the time to case completion. This paper presents a system for determining the state of an ongoing operation automatically from video. Support vector machines are trained to identify relevant image features, and hidden Markov models are trained to use these features to compute a sequence of OR states from the video. The system was tested on video captured over a 24 hour period in one of the 19 operating rooms in Baltimore's R. Adams Crowley Shock Trauma Center. It was found to be more accurate and have less delay while providing more fine-grained state information than the current state-of-the-art system based on patient vital signs used by the Shock Trauma Center.