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Guided Activity Prediction for Minimally Invasive Surgery Safety Improvement in the Internet of Medical Things | IEEE Journals & Magazine | IEEE Xplore

Guided Activity Prediction for Minimally Invasive Surgery Safety Improvement in the Internet of Medical Things


Abstract:

With the application of the Internet of Medical Things (IoMT) in minimally invasive surgery (MIS), surgeons now have a better chance at hard-to-treat cases by carrying ou...Show More

Abstract:

With the application of the Internet of Medical Things (IoMT) in minimally invasive surgery (MIS), surgeons now have a better chance at hard-to-treat cases by carrying out more complicated MIS workflows. However, a scheduled surgical workflow is often required to be updated based on the patient’s internal tissue states. Perioperative complications could occur if in-time adjustments are lacking in the operating rooms when needed. To help manage the uncertainty of live surgical workflows in the IoMT environment, we propose a MIS safety improvement framework. It helps surgeons in predicting surgical workflows with limited MIS video frames by embedding our proposed model GuidedNet. To predict future surgical activities, we first build three isomorphic neural networks to capture the spatiotemporal information. Then, we establish a guidance fusion module to handle the contextual information. It guides the GuidedNet to recognize the surgical stage. Moreover, we build a novel joint loss function to train the GuidedNet to predict the future surgical stage. We evaluate the approach on a large data set that contains 80 cholecystectomy videos (Cholec-80) and compare it with the state of the art. Experiments show that the GuidedNet can assist surgeons in carrying out MIS as well as guide the next stage of surgery for improving surgical safety. Comparing to the state of the art, our approach can obtain better predict accuracy (up to 79%) with less computing resource consumption. The result also shows that our approach has a high application prospect in video classification in other Internet of Things scenarios.
Published in: IEEE Internet of Things Journal ( Volume: 9, Issue: 6, 15 March 2022)
Page(s): 4758 - 4768
Date of Publication: 30 August 2021

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