Conference Paper

On-line Recognition of Surgical Activity for Monitoring in the Operating Room.

Conference: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008
Source: DBLP

ABSTRACT Surgery rooms are complex environments where many inter- actions take place between staff members and the electronic and mechanical systems. In spite of their inherent complex- ity, surgeries of the same kind bear numerous similarities and are usually performed with similar workflows. This gives the possibility to design support systems in the Operating Room (OR), whose applicability range from easy tasks such as the activation of OR lights and calling the next patient, to more complex ones such as context-sensitive user interfaces or au- tomatic reporting. An essential feature when designing such systems, is the ability for on-line recognition of what is hap- pening inside the OR, based on recorded signals. In this paper, we present an approach using signals from the OR and Hidden Markov Models to recognize on-line the sur- gical steps performed by the surgeon during a laparoscopic surgery. We also explain how the system can be deployed in the OR. Experiments are presented using 11 real surgeries performed by different surgeons in several ORs, recorded at our partner hospital. We believe that similar systems will quickly develop in the near future in order to efficiently support surgeons, trainees and the medical staff in general, as well as to improve admin- istrative tasks like scheduling within hospitals.

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    ABSTRACT: Abstract Introduction: Automatic surgical activity recognition in the operating room (OR) is mandatory to enable assistive surgical systems to manage the information presented to the surgical team. Therefore the purpose of our study was to develop and evaluate an activity recognition model. Material and methods: The system was conceived as a hierarchical recognition model which separated the recognition task into activity aspects. The concept used radio frequency identification (RFID) for instrument recognition and accelerometers to infer the performed surgical action. Activity recognition was done by combining intermediate results of the aspect recognition. A basic scheme of signal feature generation, clustering and sequence learning was replicated in all recognition subsystems. Hidden Markov models (HMM) were used to generate probability distributions over aspects and activities. Simulated functional endoscopic sinus surgeries (FESS) were used to evaluate the system. Results and discussion: The system was able to detect surgical activities with an accuracy of 95%. Instrument recognition performed best with 99% accuracy. Action recognition showed lower accuracies with 81% due to the high variability of surgical motions. All stages of the recognition scheme were evaluated. The model allows distinguishing several surgical activities in an unconstrained surgical environment. Future improvements could push activity recognition even further.
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    ABSTRACT: The field of surgical work-flow fosters the formalization and acquisition of surgical task descriptions from real-time surgical interven-tions to support clinical and technical analysis. However, uncertainty plays such a large part in surgical procedures that the representation of surgical work-flows need to deal with probability in a direct way. To re-duce the uncertainty in surgical task descriptions, we propose two mech-anisms to generate robust and discriminative observation sequences from a larger observation set. These observation sequences are used to train a Hidden Markov Model (HMM) with the surgical work-flow steps as hidden states and the instruments as observation set. A demonstration with ROC analysis shows that asynchronous pre-processing of the ob-servation sets leads to a better classification performance than the syn-chronous pre-processing. Hence the asynchronous pre-processing mech-anism leads to a more robust and discriminative training of the Hidden Markov Model.
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