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: Motivation: The primary economy-driven documentation of patient-specific information in clinical information systems leads to drawbacks in the use of these systems in daily clinical routine. Missing meta-data regarding underlying clinical workflows within the stored information is crucial for intelligent support systems. Unfortunately, there is still a lack of primary clinical needs-driven electronic patient documentation. Hence, physicians and surgeons must search hundreds of documents to find necessary patient data rather than accessing relevant information directly from the current process step. In this work, a completely new approach has been developed to enrich the existing information in clinical information systems with additional meta-data, such as the actual treatment phase from which the information entity originates. Methods: Stochastic models based on Hidden Markov Models (HMMs) are used to create a mathematical representation of the underlying clinical workflow. These models are created from real-world anonymized patient data and are tailored to therapy processes for patients with head and neck cancer. Additionally, two methodologies to extend the models to improve the workflow recognition rates are presented in this work. Results: A leave-one-out cross validation study was performed and achieved promising recognition rates of up to 90% with a standard deviation of 6.4%. Conclusions: The method presented in this paper demonstrates the feasibility of predicting clinical workflow steps from patient-specific information as the basis for clinical workflow support, as well as for the analysis and improvement of clinical pathways. Copyright © 2014. Published by Elsevier Inc.
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    ABSTRACT: Surgical workflow monitoring and analysis in the operating room promote safe and efficient surgical operations through developing context sensitive operating rooms, evaluating and training surgical staffs, optimizing surgeries and generating automatic reports. Existing surgical workflow analyses mostly focused on either coarse-or fine-level of granularity. To complementary fill the gap, our research aims at investigating intermediate-scale workflows using movement patterns on surgical staffs. In this paper, we introduce the ultrasonic sensor based location aware system that continuously tracks 3-dimensional positions on multiple surgical staffs in the operating room during a neurosurgical operation. In addition, we present preliminary results of trajectory data exploration for characterizing patterns on intermediate workflows as well as identifying key surgical events during a neurosurgical operation.
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