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: 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.
  • Machine Vision and Applications 01/2014; 26(1):115-125. DOI:10.1007/s00138-014-0646-x · 1.44 Impact Factor
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    ABSTRACT: The computer aided analysis of surgical activity and work-flow in the operating theatre has gained much interest in the past few years. Many of these works deal with or depend on detection and clas-sification of surgical activity which is represented by multi-dimensional, continuous signal data recorded from the Operating Room (OR). In this work, we propose a complementary approach directed towards intelligent intermediate processing of raw sensor data. We adopt a technique from data mining called motif discovery, which allows the unsupervised dis-covery of recurrent and semantically important patterns in otherwise un-structured data. Using data recorded by accelerometers placed on the op-erator, we discover an objective alphabet of surgical motions performed during simulated percutaenous vertebroplasties and autonomously iden-tify the motion pattern for surgical tool changes in laparoscopic chole-cystectomy. The results indicate the usability of motif discovery for au-tonomous pre-processing and mining of unstructured OR sensor data.

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