Phase recognition during surgical procedures using embedded and body-worn sensors

Bardram, Jakob E.; Doryab, Afsaneh; Jensen, Rune Møller; Lange, Poul M.; Nielsen, Kristian L. G.; Petersen, Soren T.

doi:10.1109/percom.2011.5767594

Cited by 64 publications

(44 citation statements)

References 19 publications

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“…Many researches have been conducted for developing methods for recognition of surgical workflow. Bardram et al [20] proposed a system using embedded and body-worn sensor data to train a decision tree in order to predict surgical phases. They studied sensor significance in order to identity the most important features for surgical phase prediction.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…Many methods have been proposed to solve the problem of automatic recognition of surgical procedures. In [11][12][13][14], the authors use instruments and sensor data directly to recognition of surgical activities. However, these methods require some special sensors and usually connected to a surgical instrument or a surgeon's hand, which may interfere with the normal operation of the operation.…”

Section: Introductionmentioning

confidence: 99%

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Semi-supervised spatio-temporal CNN for recognition of surgical workflow

Chen

Sun

Zhong

2018

J Image Video Proc.

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Robust and automated surgical workflow detection in real time is a core component of the future intelligent operating room. Based on this technology, it can help medical staff to automate and intelligently complete many routine activities during surgery. Recognition of surgical workflow based on traditional pattern recognition methods requires a large number of labeled surgical video data. However, the labeled surgical video data requires expert knowledge and it is difficult and time consuming to collect a sufficient number of labeled surgical video data in the medical field. Therefore, this paper proposes a semi-supervised spatio-temporal convolutional network for the recognition of surgical workflow based on convolutional neural networks and temporal-recursive networks. Firstly, we build a spatial convolutional extraction feature network based on unsupervised generative adversarial learning. Then, we build a bridge between lowlevel surgical video features and high-level surgical workflow semantics based on an unsupervised temporal-ordered network learning approach. Finally, we use the semi-supervised learning method to integrate the spatial model and the temporal model to fine-tune the network, and realize the intelligent recognition of the surgical workflow at a low cost to efficiently determine the progress of the surgical workflow. We performed some experiments for validating the mode based on m2cai16-workflow dataset. It shows that the proposed model can effectively extract the surgical feature and determine the surgical workflow. The Jaccard score of the model reaches 71.3%, and the accuracy of the model reaches 85.8%.

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Section: Review Of Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semi-supervised spatio-temporal CNN for recognition of surgical workflow

Chen

Sun

Zhong

2018

J Image Video Proc.

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“…In many sensor network applications, nodes are supposed to communicate with each other at the highest transmission power level to reach larger coverage area and achieve higher data delivery rate [11] [12]. However, our system is different, where only contacts within 9 feet are useful and thus it is not necessary to set mote with the highest transmission power.…”

Section: B Design Issues 1) Disease Transmission Distancementioning

confidence: 99%

Targeted vaccination based on a wireless sensor system

Sun

Zhang

et al. 2015

2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Abstract-Vaccination is one of the most effective ways to protect people from being infected by infectious disease. However, it is often impractical to vaccinate all people in a community due to various resource constraints. Therefore, targeted vaccination, which vaccinates a small group of people, is an alternative approach to contain infectious disease spread. To achieve better performance in targeted vaccination, we collect student contact traces in a high school based on wireless sensors carried by students. With our wireless sensor system, we can record student contacts within the disease propagation distance, and then construct a disease propagation graph to model the infectious disease propagation. Based on this graph, we propose a metric called connectivity centrality to measure a node's importance during disease propagation and design centrality based algorithms for targeted vaccination. The proposed algorithms are evaluated and compared with other schemes based on our collected traces. Trace driven simulation results show that our algorithms can help to effectively contain infectious disease.

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“…This process requires interaction with medical objects, most of which are uniquely associated with tasks. Object-use information has been applied for tracking hospital activities [17] and classifying surgery phases [18].…”

Section: Application Domain: Trauma Resuscitationmentioning

confidence: 99%

Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study

Parlak

Marsic

2013

IEEE Trans. Instrum. Meas.

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Abstract-We studied object motion detection in an indoor environment using RFID technology. Unlike prior work, we focus on dynamic scenarios, such as emergency medical situations, subject to signal interference by people and many RFID tags. We build a realistic trauma resuscitation setting and record a dataset of around 14 000 detection instances. We find that factors affecting radio signal, such as tag motion, have different statistical fingerprints, making them discernible using statistical methods. Our method for object motion detection extracts descriptive features of the received signal strength and classifies them using machine-learning techniques. We report experimental results obtained with several statistical features and classifiers, and provide guidelines for feature and classifier selection in different environments. Experimental results show that object motion could be detected with an accuracy of 80% in complex scenarios and 90% on average. The motion type, on the other hand, could not be identified with such high accuracy using currently available passive RFID technology.

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Phase recognition during surgical procedures using embedded and body-worn sensors

Cited by 64 publications

References 19 publications

Semi-supervised spatio-temporal CNN for recognition of surgical workflow

Semi-supervised spatio-temporal CNN for recognition of surgical workflow

Targeted vaccination based on a wireless sensor system

Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study

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