Semi-manual mastoidectomy assisted by human–robot collaborative control – A temporal bone replica study

Lim, Hoon; Matsumoto, Nozomu; Cho, Byunghyun; Hong, Jin; Yamashita, Makoto; Hashizume, Makoto; Yi, Byung Ju

doi:10.1016/j.anl.2015.08.008

Cited by 12 publications

(14 citation statements)

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“…Second, our robot can be used to train less‐experienced surgeons, including residents and fellows, in the mastoidectomy procedure. Experienced surgeons rarely produce damage to critical structures; however, for inexperienced surgeons, drilling through the complex anatomy of the temporal bone may be challenging . In terms of time, safety, and thickness, inexperienced surgeons showed relatively worse results than experienced surgeons in our experiment.…”

Section: Discussionmentioning

confidence: 99%

“…The experimental settings in the present study were similar to those adopted by the previous report on a temporal bone replica . Preoperative CT image acquisition was the first step in the procedure.…”

Section: Methodsmentioning

confidence: 99%

“…A preoperative CT dataset of the cadaver was input into an open‐source software program for image guidance, as previously reported (3DSlicer, http://www.slicer.org). The structures requiring protection, such as the region of the facial nerve in the temporal bone, from the first genu to the level of the mastoid tip, were segmented and extracted as a model comprising a finite number of spheres. The sigmoid sinus, jugular bulb, internal carotid artery, middle fossa dura, and labyrinth, including semicircular canals, were also separately segmented in a similar manner.…”

Section: Methodsmentioning

confidence: 99%

“…In the present study, we developed a statically balanced surgical robot that uses pre‐set bone structures to set boundaries for the surgeon. A previous temporal bone replica study has assessed the feasibility of using a serial‐type robot system . Here we investigated the safety and feasibility of our statically balanced surgical robot system compared to traditional mastoidectomy and describe our preclinical findings in cadaveric experiments.…”

Section: Introductionmentioning

confidence: 99%

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A cadaver study of mastoidectomy using an image‐guided human–robot collaborative control system

Yoo

Lee

Yang

et al. 2017

Laryngoscope Investig Oto

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ObjectiveSurgical precision would be better achieved with the development of an anatomical monitoring and controlling robot system than by traditional surgery techniques alone. We evaluated the feasibility of robot‐assisted mastoidectomy in terms of duration, precision, and safety.Study DesignHuman cadaveric study.Materials and MethodsWe developed a multi‐degree‐of‐freedom robot system for a surgical drill with a balancing arm. The drill system is manipulated by the surgeon, the motion of the drill burr is monitored by the image‐guided system, and the brake is controlled by the robotic system. The system also includes an alarm as well as the brake to help avoid unexpected damage to vital structures. Experimental mastoidectomy was performed in 11 temporal bones of six cadavers. Parameters including duration and safety were assessed, as well as intraoperative damage, which was judged via pre‐ and post‐operative computed tomography.ResultsThe duration of mastoidectomy in our study was comparable with that required for chronic otitis media patients. Although minor damage, such as dura exposure without tearing, was noted, no critical damage to the facial nerve or other important structures was observed. When the brake system was set to 1 mm from the facial nerve, the postoperative average bone thicknesses of the facial nerve was 1.39, 1.41, 1.22, 1.41, and 1.55 mm in the lateral, posterior pyramidal and anterior, lateral, and posterior mastoid portions, respectively.ConclusionMastoidectomy can be successfully performed using our robot‐assisted system while maintaining a pre‐set limit of 1 mm in most cases. This system may thus be useful for more inexperienced surgeons.Level of EvidenceNA.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A cadaver study of mastoidectomy using an image‐guided human–robot collaborative control system

Yoo

Lee

Yang

et al. 2017

Laryngoscope Investig Oto

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“…Recently, Lim et al developed a robot for otological surgery that uses human-robot collaborative control. 15 Our system eliminates the surgeon's involvement in the drilling steps once the drilling path information has been entered. The goal in developing and using these robotic systems is to reduce the duration of surgeries and to minimize human error and the overall cost of surgeries.…”

Section: Figmentioning

confidence: 99%

Computer-aided design/computer-aided manufacturing skull base drill

Couldwell¹,

MacDonald²,

Thomas

et al. 2017

Neurosurgical Focus

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The authors have developed a simple device for computer-aided design/computer-aided manufacturing (CAD-CAM) that uses an image-guided system to define a cutting tool path that is shared with a surgical machining system for drilling bone. Information from 2D images (obtained via CT and MRI) is transmitted to a processor that produces a 3D image. The processor generates code defining an optimized cutting tool path, which is sent to a surgical machining system that can drill the desired portion of bone. This tool has applications for bone removal in both cranial and spine neurosurgical approaches. Such applications have the potential to reduce surgical time and associated complications such as infection or blood loss. The device enables rapid removal of bone within 1 mm of vital structures. The validity of such a machining tool is exemplified in the rapid (< 3 minutes machining time) and accurate removal of bone for transtemporal (for example, translabyrinthine) approaches.

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A Self‐Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone‐Beam CT Imaging

Ding

et al. 2023

Otolaryngol.--head neck surg.

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ObjectivePreoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time‐consuming. Automated methods for segmenting multiple geometrically complex structures can not only streamline preoperative planning but also augment minimally invasive and/or robot‐assisted procedures in this space. This study evaluates a state‐of‐the‐art deep learning pipeline for semantic segmentation of temporal bone anatomy.Study DesignA descriptive study of a segmentation network.SettingAcademic institution.MethodsA total of 15 high‐resolution cone‐beam temporal bone computed tomography (CT) data sets were included in this study. All images were co‐registered, with relevant anatomical structures (eg, ossicles, inner ear, facial nerve, chorda tympani, bony labyrinth) manually segmented. Predicted segmentations from no new U‐Net (nnU‐Net), an open‐source 3‐dimensional semantic segmentation neural network, were compared against ground‐truth segmentations using modified Hausdorff distances (mHD) and Dice scores.ResultsFivefold cross‐validation with nnU‐Net between predicted and ground‐truth labels were as follows: malleus (mHD: 0.044 ± 0.024 mm, dice: 0.914 ± 0.035), incus (mHD: 0.051 ± 0.027 mm, dice: 0.916 ± 0.034), stapes (mHD: 0.147 ± 0.113 mm, dice: 0.560 ± 0.106), bony labyrinth (mHD: 0.038 ± 0.031 mm, dice: 0.952 ± 0.017), and facial nerve (mHD: 0.139 ± 0.072 mm, dice: 0.862 ± 0.039). Comparison against atlas‐based segmentation propagation showed significantly higher Dice scores for all structures (p < .05).ConclusionUsing an open‐source deep learning pipeline, we demonstrate consistently submillimeter accuracy for semantic CT segmentation of temporal bone anatomy compared to hand‐segmented labels. This pipeline has the potential to greatly improve preoperative planning workflows for a variety of otologic and neurotologic procedures and augment existing image guidance and robot‐assisted systems for the temporal bone.

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Semi-manual mastoidectomy assisted by human–robot collaborative control – A temporal bone replica study

Cited by 12 publications

References 14 publications

A cadaver study of mastoidectomy using an image‐guided human–robot collaborative control system

A cadaver study of mastoidectomy using an image‐guided human–robot collaborative control system

Computer-aided design/computer-aided manufacturing skull base drill

A Self‐Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone‐Beam CT Imaging

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