Patient positioning by visualising surgical robot rotational workspace in augmented reality

Żelechowski, Marek; Karnam, Murali; Faludi, Balázs; Gerig, Nicolas; Rauter, Georg; Cattin, Philippe C.

doi:10.1080/21681163.2021.2002192

Cited by 10 publications

(11 citation statements)

References 20 publications

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“…Although there is limited XR research focusing on clinically relevant endpoints, the field that has made the most progress in terms of meaningful clinical implementation is postoperative rehabilitation, as XR platforms have demonstrated correlations with reduced postoperative pain, improved functional performance, and decreased psychological stress 61 . Extended reality technologies can also enable remote assistance for complex cases in low- and middle-income countries, 13 facilitate patient rounds, 16 convert computed tomography images to XR for surgical planning, 64 and decrease surgical setup time 65 …”

Section: Discussionmentioning

confidence: 99%

Does Extended Reality Simulation Improve Surgical/Procedural Learning and Patient Outcomes When Compared With Standard Training Methods?

Woodall,

Chang,

Toy

et al. 2024

Sim Healthcare

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Introduction The use of extended reality (XR) technologies, including virtual, augmented, and mixed reality, has increased within surgical and procedural training programs. Few studies have assessed experiential learning- and patient-based outcomes using XR compared with standard training methods. Methods As a working group for the Society for Simulation in Healthcare, we used Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and a PICO strategy to perform a systematic review of 4238 articles to assess the effectiveness of XR technologies compared with standard training methods. Outcomes were grouped into knowledge, time-to-completion, technical proficiency, reactions, and patient outcomes. Because of study heterogeneity, a meta-analysis was not feasible. Results Thirty-two studies met eligibility criteria: 18 randomized controlled trials, 7 comparative studies, and 7 systematic reviews. Outcomes of most studies included Kirkpatrick levels of evidence I–III (reactions, knowledge, and behavior), while few reported level IV outcomes (patient). The overall risk of bias was low. With few exceptions, included studies showed XR technology to be more effective than standard training methods in improving objective skills and performance, shortening procedure time, and receiving more positive learner ratings. However, XR use did not show significant differences in gained knowledge. Conclusions Surgical or procedural XR training may improve technical skill development among trainees and is generally favored over standard training methods. However, there should be an additional focus on how skill development translates to clinically relevant outcomes. We recommend longitudinal studies to examine retention and transfer of training to clinical settings, methods to improve timely, adaptive feedback for deliberate practice, and cost analyses.

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Section: Discussionmentioning

confidence: 99%

Does Extended Reality Simulation Improve Surgical/Procedural Learning and Patient Outcomes When Compared With Standard Training Methods?

Woodall,

Chang,

Toy

et al. 2024

Sim Healthcare

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“…Within the scope of the previous work, we demonstrated the benefits of using augmented reality in patient positioning tasks [ 36 ]. The users were tasked with positioning the virtual patient while being able to preview the robot’s rotational workspace using the visualization modes (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Automatic patient positioning based on robot rotational workspace for extended reality

et al. 2023

Self Cite

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Purpose Understanding the properties and aspects of the robotic system is essential to a successful medical intervention, as different capabilities and limits characterize each. Robot positioning is a crucial step in the surgical setup that ensures proper reachability to the desired port locations and facilitates docking procedures. This very demanding task requires much experience to master, especially with multiple trocars, increasing the barrier of entry for surgeons in training. Methods Previously, we demonstrated an Augmented Reality-based system to visualize the rotational workspace of the robotic system and proved it helps the surgical staff to optimize patient positioning for single-port interventions. In this work, we implemented a new algorithm to allow for an automatic, real-time robotic arm positioning for multiple ports. Results Our system, based on the rotational workspace data of the robotic arm and the set of trocar locations, can calculate the optimal position of the robotic arm in milliseconds for the positional and in seconds for the rotational workspace in virtual and augmented reality setups. Conclusions Following the previous work, we extended our system to support multiple ports to cover a broader range of surgical procedures and introduced the automatic positioning component. Our solution can decrease the surgical setup time and eliminate the need to repositioning the robot mid-procedure and is suitable both for the preoperative planning step using VR and in the operating room—running on an AR headset.

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“…We ablate the model components and find that the use of our generalized architecture improves the results over the HookNet and MRN variants. We will use our bone segmentation in conjunction with 3D rendering of anatomical images in augmented-and virtual reality applications, where segmentations can be used on top or in conjunction with existing transfer functions [17,18].…”

Section: Introductionmentioning

confidence: 99%

Improved distinct bone segmentation in upper-body CT through multi-resolution networks

Schnider¹,

Wolleb²,

Huck³

et al. 2023

Preprint

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Automated distinct bone segmentation from CT scans is widely used in planning and navigation workflows. U-Net variants are known to provide excellent results in supervised semantic segmentation. However, in distinct bone segmentation from upper body CTs a large field of view and a computationally taxing 3D architecture are required. This leads to low-resolution results lacking detail or localisation errors due to missing spatial context when using high-resolution inputs. Methods: We propose to solve this problem by using end-to-end trainable segmentation networks that combine several 3D U-Nets working at different resolutions. Our approach, which extends and generalizes HookNet and MRN, captures spatial information at a lower resolution and skips the encoded information to the target network, which operates on smaller high-resolution inputs. We evaluated our proposed architecture against single resolution networks and performed an ablation study on information concatenation and the number of context networks. Results: Our proposed best network achieves a median DSC of 0.86 taken over all 125 segmented bone classes and reduces the confusion among similar-looking bones in different locations. These results outperform our previously published 3D U-Net baseline results on the task and distinct-bone segmentation results reported by other groups.

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Patient positioning by visualising surgical robot rotational workspace in augmented reality

Cited by 10 publications

References 20 publications

Does Extended Reality Simulation Improve Surgical/Procedural Learning and Patient Outcomes When Compared With Standard Training Methods?

Does Extended Reality Simulation Improve Surgical/Procedural Learning and Patient Outcomes When Compared With Standard Training Methods?

Automatic patient positioning based on robot rotational workspace for extended reality

Improved distinct bone segmentation in upper-body CT through multi-resolution networks

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