Development and Initial Clinical Testing of “OPECT”

Yoshimitsu, Kitaro; Muragakı, Yoshihiro; Maruyama, Takashi; Yamato, Masayuki; Iseki, Hiroshi

doi:10.1227/neu.0000000000000214

Cited by 14 publications

(3 citation statements)

References 11 publications

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“…Conversely, misrecognition may occur due to environmental sounds (noise) during voice operation; however, after implementing countermeasures, the interface became sufficiently applicable for clinical use. In the field of neurosurgery, only image-assisted surgery using Kinect gesture recognition in craniotomy has been reported [12]. However, to the best of our knowledge, no similar reports have been published for endovascular treatment, including endovascular neurosurgery.…”

Section: Discussionmentioning

confidence: 99%

“…Its clinical application has been reported for abdominal surgery [3][4][5], percutaneous biopsy [6], examination assistance [7,8], rehabilitation [9,10], and medical education [11]. In the field of neurosurgery, only one study has applied this kind of navigation system during a craniotomy [12]. Kinect is a low-cost device developed by Microsoft that provides a joystick-free game with gesture recognition, depth camera, accelerometers, and other features.…”

Section: Introductionmentioning

confidence: 99%

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Development and clinical evaluation of a contactless operating interface for three-dimensional image-guided navigation for endovascular neurosurgery

et al. 2021

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Purpose In endovascular neurosurgery, the operator often acquires three-dimensional (3D) images of the cerebral vessels. Although workstation reoperation is required in some situations during treatment, it leads to time loss because a sterile condition cannot be maintained and treatment must be temporarily interrupted. Therefore, a workstation reoperating system is required while maintaining the desired sterility. Methods A contactless operating interface using Kinect to control 3D images was developed via gesture recognition for endovascular neurosurgery and was applied to a 3D volume rendering technique (VRT) image reconstructed at the workstation. The left-hand movement determines the assigned functions, whereas the right-hand movement is used like a computer mouse to pan and zoom in/out. In addition to the interface, voice commands were used and assigned to digital operations, such as image view changes and mode signal changes. Results This system was used for the actual endovascular treatment of cerebral aneurysms and cerebral arteriovenous malformations. The operator and gesture were recognized without any problems. Using voice operation, it was possible to expeditiously set the VRT image back to the reference angle. Furthermore, it was possible to finely adjust gesture operations, including mouse operation, and treatment was completed while maintaining sterile conditions. Conclusion A contactless operating interface was developed by combining the existing workstation system with Kinect and voice recognition software, allowing surgeons to perform a series of operations, which are normally performed in a console room, while maintaining sterile conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and clinical evaluation of a contactless operating interface for three-dimensional image-guided navigation for endovascular neurosurgery

et al. 2021

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“…In recent years, the evaluation of hospital effectiveness has gained increased attention with the introduction of new surgical techniques and procedures, such as surgical video database development and real-time tool-usage signal management [ 1 , 2 , 3 , 4 , 5 , 6 ]. In particular, there are studies aiming at recognizing and segmenting surgical workflow to assess efficiency inside an operating room (OR) and the skills of the surgical staff [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation

Luo

Nara

Izumi

2021

IJERPH

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Recognizing and segmenting surgical workflow is important for assessing surgical skills as well as hospital effectiveness, and plays a crucial role in maintaining and improving surgical and healthcare systems. Most evidence supporting this remains signal-, video-, and/or image-based. Furthermore, casual evidence of the interaction between surgical staff remains challenging to gather and is largely absent. Here, we collected the real-time movement data of the surgical staff during a neurosurgery to explore cooperation networks among different surgical roles, namely surgeon, assistant nurse, scrub nurse, and anesthetist, and to segment surgical workflows to further assess surgical effectiveness. We installed a zone position system (ZPS) in an operating room (OR) to effectively record high-frequency high-resolution movements of all surgical staff. Measuring individual interactions in a closed, small area is difficult, and surgical workflow classification has uncertainties associated with the surgical staff in terms of their varied training and operation skills, patients in terms of their initial states and biological differences, and surgical procedures in terms of their complexities. We proposed an interaction-based framework to recognize the surgical workflow and integrated a Bayesian network (BN) to solve the uncertainty issues. Our results suggest that the proposed BN method demonstrates good performance with a high accuracy of 70%. Furthermore, it semantically explains the interaction and cooperation among surgical staff.

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