Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Intelligent Control of Robotic X-ray Devices using a Language-promptable Digital Twin

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A voice-controlled robotic C-arm can aim, collimate, and zoom by name using a language-promptable digital twin, with 83.54% end-to-end success in a cadaver study.

desk verdict A solid proof-of-concept for voice-controlled robotic C-arms, but the headline accuracy figures are conditioned on the segmentation model succeeding and the end-to-end success is padded by easy low-level commands. read the letter →

arxiv 2412.08020 v1 pith:EG6PHALA submitted 2024-12-11 cs.RO cs.AIcs.CVcs.HCcs.LG

Benjamin D. Killeen , Anushri Suresh , Catalina Gomez , Blanca Inigo , Christopher Bailey , Mathias Unberath This is my paper · ORCID
classification cs.ROcs.AIcs.CVcs.HCcs.LG
keywords roboticC-armdigitaltwinnaturallanguageinterfaceFluoroSAMtext-promptedsegmentationvoicecontrolimage-guidedsurgeryX-raycollimation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a robotic C-arm X-ray system can be steered by natural spoken language when the system maintains a patient-specific digital twin built from text-prompted segmentations of previously acquired X-rays. The authors connect a large language model, a text-promptable X-ray segmentation model called FluoroSAM, and a sparse 3D reconstruction that tracks the requested anatomy in patient coordinates. In a cadaver study, the integrated system succeeded end-to-end on 83.54% of 158 spoken prompts, and in post hoc analysis the digital twin localized 35 commonly requested structures to within $51.68 \pm 30.84$ mm with bounding box recall $0.70 \pm 0.26$. If correct, this matters because physicians could express imaging intent directly, letting the C-arm aim, collimate, and visualize without manual positioning commands, which could reduce radiation exposure and streamline image-guided procedures.

What carries the argument

The load-bearing mechanism is the language-promptable digital twin, a sparse 3D point set computed by backprojecting FluoroSAM text-prompted segmentation logits from two to five X-ray images chosen for viewpoint diversity (at least 30 degrees apart). A voxel in the 3 mm isocentric grid survives when its mean logit across the contributing masks is at least 0.5 and it appears in at least two masks. This point cloud lives in patient coordinates, so it can define a collimation box, a viewing direction, or a highlighted overlay for the next acquisition. The LLM supplies the action and prompt, FluoroSAM supplies the masks, and the digital twin supplies the geometry that turns language into a concrete C-arm motion.

What would settle it

Run the same 1990-image-subset protocol on prompts with DICE below 0.3, such as 'L4 vertebra bone' or 'right kidney', and compare the digital twin's 3D centroid error and bounding box recall; if they degrade far beyond $51.68 \pm 30.84$ mm and 0.70, the 'arbitrary orientations' claim does not extend to those anatomies.

Watch

Extended reading notes

Core claim

The central claim is that a language-aligned foundation model for X-ray segmentation is enough to power a voice-controlled robotic C-arm, provided the system keeps a continuously updated digital twin. An LLM turns each spoken command into a machine-readable action plus a concise anatomy prompt; FluoroSAM segments that anatomy in recent X-rays; and those segmentation masks are backprojected into a 3 mm-grid patient volume, keeping points seen in at least two masks, to define the anatomy's location. The paper reports that users could visualize, collimate, and view-find across the torso by voice, with 83.54% end-to-end success over 158 prompts. In a post hoc evaluation using 1990 random subsets of images, the digital twin localized the 35 tested structures with a 3D centroid error of $51.68 \pm 30.84$ mm and bounding box recall of $0.70 \pm 0.26$, which the authors take to show localization and isolation from arbitrary orientations for structures that the segmenter handles well.

Load-bearing premise

The whole pipeline depends on FluoroSAM producing a correct text-only segmentation of the named structure, and the reported localization and collimation numbers cover only structures where its DICE score (a standard measure of segmentation overlap) is above 0.3; small or repeating structures such as individual vertebrae, ribs, kidneys, and clavicles fall below that threshold and are not demonstrated.

Editorial extensions

If this is right

  • Automatic collimation can keep the X-ray field limited to the requested anatomy, which is a direct path to lower radiation exposure in image-guided procedures.
  • Patient-specific viewfinding lets the C-arm move to AP, lateral, or previously used views based on the reconstructed anatomy, without the physician specifying angles or coordinates.
  • The same language layer can mix low-level motions, like 'roll over 30 degrees', with high-level requests, like 'focus on the lower lumbar vertebrae', so one interface covers routine and complex control.
  • Because LLM interpretation takes under a second while C-arm motion and acquisition take ten seconds or more, the language interface does not add a practical latency bottleneck.
  • Success rates were similar for the two accents tested (87.5% and 82.2%), suggesting the voice interface is usable across speakers, though speech-to-text errors remain a real failure source.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: replacing FluoroSAM with a stronger text-prompted segmenter would extend the same digital twin to currently failing anatomies (individual vertebrae, ribs, kidneys, clavicles) without changing the LLM or reconstruction machinery.
  • Inference: because the localization and recall figures are restricted to prompts with DICE above 0.3, the practical success rate over all spoken requests is probably lower than the reported 83.54%.
  • Inference: the patient-coordinate digital twin could double as a running anatomy map for dose tracking or automated procedural documentation, since it already stores where requested structures are.
  • Inference: a testable extension would use the digital twin's bounding box to drive automatic C-arm positioning rather than only collimation, measuring procedure time or retake rate against manual control.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper presents a voice-controlled robotic C-arm system that combines an LLM (GPT-4o) for parsing spoken commands, Whisper for speech-to-text, and FluoroSAM, a language-promptable X-ray segmentation model, to maintain a patient digital twin from sparse intraoperative images. High-level commands trigger visualization, automatic collimation, and patient-specific viewfinding. The system is evaluated in a cadaver study with 158 spoken prompts (83.54% overall success) and post hoc on 1990 image subsets from 46 unique X-ray views, reporting a 3D centroid error of 51.68 ± 30.84 mm and bounding-box recall of 0.70 ± 0.26 for 35 prompts. The authors position the work as a proof of concept for language-driven autonomous C-arm control.

Significance. If the reported results are interpreted with the appropriate caveats, the paper is a valuable proof-of-concept for integrating large language models, speech interfaces, and X-ray foundation models into robotic C-arm control. The strengths include a real cadaver study with ground truth derived from post-study cone-beam CT via TotalSegmentator, detailed per-prompt performance tables in the appendix, and an honest discussion of FluoroSAM's text-only failure modes. The system integration itself is non-trivial and the end-to-end demonstration, including different speaker accents, is a useful data point for the community. The paper does not claim a new segmentation model, and its central contribution is the pipeline and feasibility demonstration rather than a breakthrough in localization accuracy.

major comments (3)
  1. [Abstract and §4.2] The headline 3D localization numbers (51.68 ± 30.84 mm centroid error, 0.70 ± 0.26 bounding-box recall) are computed only on the 1990 image subsets for which FluoroSAM's text-only DICE exceeds 0.3. This filter is applied post hoc, but DICE is not available at run time, so the reported accuracy is conditional on successful segmentation. Table A1 shows that several commonly requested structures fall below this threshold, including 'L5 vertebra bone' (DICE 0.25), 'L3 vertebra bone' (0.21), 'Right kidney' (0.19), 'Sternum bone' (0.17), and 'L4 vertebra bone' (0.16). The abstract's claim that the digital twin 'could localize 35 commonly requested structures ... enabling localization and isolation from arbitrary orientations' is therefore not established for the full prompt space. Please either report unconditional performance over all evaluated prompts, or explicitly state the DICE>0.3 conditioning in the abstract and main-text results and list which structures are excluded.
  2. [Abstract and §4.3] The abstract's '84% end-to-end success' conflates low-level and high-level commands. Of the 158 prompts, only 86 are high-level (46 visualization, 28 collimation, 12 viewfinding); the remaining 72 are low-level commands such as 'take a shot' or 'roll over 30 degrees'. The per-category success rates are 82.6% for visualization, 83.3% for viewfinding, but only 60.7% (17/28) for collimation, which is one of the paper's three headline high-level capabilities. The overall 83.54% figure is thus dominated by simpler low-level actions. Please report the overall rate together with per-category breakdowns in the abstract, or avoid implying that the full system succeeds on 84% of high-level requests.
  3. [§3.1, Eq. (1)] The sparse reconstruction X in Eq. (1) is defined by backprojecting heatmaps only into the frustum of the current image I0, and each point must project inside P0's field of view. The centroid error is then measured 'relative to its overlap with the current image.' For structures that extend outside the current image, the error may be partially bounded by the image frustum rather than by the 3D reconstruction itself. Additionally, the 'arbitrary orientations' claim is constrained by the 30° minimum angular-separation criterion used for image selection. Please clarify how much of the reported localization error is attributable to the image-frustum constraint, and report the angular coverage of the 46 unique views used in the study.
minor comments (5)
  1. [Abstract] The abstract reports '51.68 mm' without the standard deviation; for consistency with §4.2, it should read '51.68 ± 30.84 mm'.
  2. [§4.3] The sentence '38 / 46 (82.6%) visualization actions, 17 / 28 (60.7%) collimations, and 10 / 12 (83.33%)' omits the word 'view' before 'actions' for the third quantity; also, stating that 'the remainder' is 72 low-level prompts would make the composition of the 158 prompts explicit.
  3. [§4.2] The phrase 'individual femur pones' appears to be a typo for 'individual femur bones'.
  4. [§3.1] The text contains minor typographical errors, including 'image being examing' and the incomplete address '212187' in the author affiliation; these should be corrected.
  5. [Appendix A] Table A1 is described as the 'top 40 prompts' but contains 41 rows, while Table A2 contains 35 rows; please clarify the selection procedure and report the exact number of prompts in each analysis.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation found: the digital twin's localization and collimation accuracy is evaluated against external CT-derived ground truth, and the DICE filter is an openly stated scope condition rather than a fitted input.

full rationale

The paper's claimed derivation chain is not circular. The digital twin is defined in Eq. 1 as a direct thresholded backprojection of FluoroSAM logits, and its accuracy is measured against TotalSegmentator masks projected from a post-study cone-beam CT (Section 4), an external ground truth independent of FluoroSAM's own outputs. No fitted parameter is renamed as a prediction; the reconstruction is deterministic once the segmentation logits and projection matrices are given. The Section 4.2 restriction to image subsets with FluoroSAM DICE > 0.3 is explicitly framed as an isolation of digital-twin performance 'given a reasonable segmentation'; this conditions the headline 51.68 ± 30.84 mm and recall 0.70 numbers on a successful segmentation, which is a scope limitation acknowledged in Section 5 ('the performance of the foundation model, FluoroSAM, is severely limited when used without additional point prompts'), not a circular reduction. Self-citations to the authors' prior FluoroSAM paper [9] and protocol papers [1, 3] supply components, but those components are re-evaluated here against CT ground truth and live cadaver performance, so the citations are not load-bearing in a way that makes the result equivalent to its inputs.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities. Its central claim rests on the reliability of FluoroSAM text-only segmentation, which is externally evaluated on real X-rays against CT ground truth, but only for a post hoc selected set of prompts.

free parameters (4)
  • DICE > 0.3 inclusion threshold for 3D evaluation = 0.3
    The 51.68 mm localization error and bounding box statistics are computed only for prompts where FluoroSAM's DICE exceeds 0.3; this post hoc selection raises the reported performance and excludes 5 of the top 40 prompts (Table A2).
  • Top-40 prompt selection
    FluoroSAM performance is measured on the 40 best prompts from an attending radiologist; the 3D evaluation is restricted to these prompts, so the reported accuracy is conditional on the model's best cases.
  • Mask probability threshold (mean f >= 0.5) = 0.5
    Points are included in the digital twin if the mean FluoroSAM logit is at least 0.5 and present in at least 2 masks (Eq. 1); this threshold is chosen by hand and affects reconstruction coverage.
  • Minimum angular separation (30 degrees) for image selection = 30 degrees
    Images closer than 30 degrees are replaced by the most recent to maintain viewpoint diversity; this heuristic affects the conditioning of the 3D reconstruction.
assumptions (3)
  • domain assumption Projection matrices P_i are known accurately via optical navigation for all images.
    Equation (1) assumes known camera poses relative to a fixed coordinate system (optical marker); any registration error directly degrades the digital twin reconstruction (Section 3.1).
  • domain assumption TotalSegmentator organ segmentations on the post-study cone-beam CT are valid ground truth for real cadaver anatomy.
    Ground truth masks are generated by stitching 4 navigated CBCTs and projecting TotalSegmentator segmentations (Section 4); these are the same annotation conventions used to train FluoroSAM on synthetic DRRs, so the evaluation is aligned with the model's training distribution.
  • ad hoc to paper A FluoroSAM DICE score above 0.3 is sufficient for reliable 3D localization.
    The 3D evaluation is restricted to prompts with DICE > 0.3 (Section 4.2); this threshold is defined for this study and is not justified by an independent criterion.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Intelligent Control of Robotic X-ray Devices using a Language-promptable Digital Twin." pith.science (2026). https://pith.science/paper/EG6PHALA

@misc{pith2026241208020,
  author       = {Pith},
  title        = {Pith review of: Intelligent Control of Robotic X-ray Devices using a Language-promptable Digital Twin},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EG6PHALA}},
  note         = {Machine review of arXiv:2412.08020}
}
read the original abstract

Natural language offers a convenient, flexible interface for controlling robotic C-arm X-ray systems, making advanced functionality and controls accessible. However, enabling language interfaces requires specialized AI models that interpret X-ray images to create a semantic representation for reasoning. The fixed outputs of such AI models limit the functionality of language controls. Incorporating flexible, language-aligned AI models prompted through language enables more versatile interfaces for diverse tasks and procedures. Using a language-aligned foundation model for X-ray image segmentation, our system continually updates a patient digital twin based on sparse reconstructions of desired anatomical structures. This supports autonomous capabilities such as visualization, patient-specific viewfinding, and automatic collimation from novel viewpoints, enabling commands 'Focus in on the lower lumbar vertebrae.' In a cadaver study, users visualized, localized, and collimated structures across the torso using verbal commands, achieving 84% end-to-end success. Post hoc analysis of randomly oriented images showed our patient digital twin could localize 35 commonly requested structures to within 51.68 mm, enabling localization and isolation from arbitrary orientations. Our results demonstrate how intelligent robotic X-ray systems can incorporate physicians' expressed intent directly. While existing foundation models for intra-operative X-ray analysis exhibit failure modes, as they improve, they can facilitate highly flexible, intelligent robotic C-arms.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Intelligent Virtual Sonographer (IVS): Enhancing Physician-Robot-Patient Communication

    cs.HC 2025-07 conditional novelty 6.0 of 10

    IVS is a dual-LLM virtual sonographer in XR that mediates physician-robot-patient communication during robotic ultrasound, with a 14-participant pilot showing high command accuracy and positive ratings.

Reference graph

Works this paper leans on

31 extracted references · 16 canonical work pages · cited by 1 Pith paper

  1. [1]

    Killeen, B.D., et al.: Take a shot! Natural language control of intelligent robotic X-ray systems in surgery. Int. J. CARS 19(6), 1165–1173 (2024) https://doi.org/ 10.1007/s11548-024-03120-3

    doi:10.1007/s11548-024-03120-3 2024

  2. [2]

    Kausch, L., et al.: Toward automatic C-arm positioning for standard projections in orthopedic surgery. Int. J. CARS 15(7), 1095–1105 (2020) https://doi.org/10. 1007/s11548-020-02204-0

    work page 2020

  3. [3]

    Killeen, B.D., et al.: An autonomous X-ray image acquisition and interpretation system for assisting percutaneous pelvic fracture fixation. Int. J. CARS 18(7), 1201–1208 (2023) https://doi.org/10.1007/s11548-023-02941-y

    doi:10.1007/s11548-023-02941-y 2023

  4. [4]

    In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, pp

    Killeen, B.D., et al.: Pelphix: Surgical Phase Recognition from X-Ray Images in Percutaneous Pelvic Fixation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, pp. 133–143. Springer, Cham, Switzerland (2023). https://doi.org/10.1007/978-3-031-43996-4 13

    doi:10.1007/978-3-031-43996-4 2023

  5. [5]

    Kawaharazuka, K., et al.: Real-world robot applications of foundation models: a review. Adv. Rob. (2024)

    work page 2024

  6. [6]

    10 arXiv (2021) https://doi.org/10.48550/arXiv.2108.07258 2108.07258

    Bommasani, R., et al.: On the Opportunities and Risks of Foundation Models. 10 arXiv (2021) https://doi.org/10.48550/arXiv.2108.07258 2108.07258

    arXiv doi:10.48550/arxiv.2108.07258 2021

  7. [7]

    arxiv 2024

    Chen, Z., et al.: Chexagent: Towards a foundation model for chest x-ray interpretation. arxiv 2024. arXiv preprint arXiv:2401.12208

    arXiv 2024

  8. [8]

    Ma, J., et al.: Segment anything in medical images. Nat. Commun. 15(654), 1–9 (2024) https://doi.org/10.1038/s41467-024-44824-z

    doi:10.1038/s41467-024-44824-z 2024

Show all 31 references
  1. [9]

    arXiv (2024) https://doi.org/10.48550/arXiv.2403.08059 2403.08059

    Killeen, B.D., et al.: FluoroSAM: A Language-aligned Foundation Model for X-ray Image Segmentation. arXiv (2024) https://doi.org/10.48550/arXiv.2403.08059 2403.08059

    2024 doi arXiv

  2. [10]

    Annual Review of Control, Robotics, and Autonomous Systems 3(1), 25–55 (2020)

    Tellex, S., et al.: Robots that use language. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 25–55 (2020)

    2020

  3. [11]

    IEEE Access (2024)

    Vemprala, S.H., et al.: Chatgpt for robotics: Design principles and model abilities. IEEE Access (2024)

    2024

  4. [12]

    In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp

    Dai, Y., et al.: Think, act, and ask: Open-world interactive personalized robot navigation. In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 3296–3303 (2024). IEEE

    2024

  5. [13]

    IEEE Robotics and Automation Letters (2023)

    Lynch, C., et al.: Interactive language: Talking to robots in real time. IEEE Robotics and Automation Letters (2023)

    2023

  6. [14]

    arXiv preprint arXiv:2409.13393 (2024)

    Martinez-Baselga, D., et al.: Hey robot! personalizing robot navigation through model predictive control with a large language model. arXiv preprint arXiv:2409.13393 (2024)

    2024 arXiv

  7. [15]

    In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp

    Bucker, A., et al.: Reshaping robot trajectories using natural language commands: A study of multi-modal data alignment using transformers. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 978–984 (2022). IEEE

    2022

  8. [16]

    In: Conference on Robot Learning, pp

    Shridhar, M., et al.: Cliport: What and where pathways for robotic manipulation. In: Conference on Robot Learning, pp. 894–906 (2022). PMLR

    2022

  9. [17]

    Nature 616(7956), 259–265 (2023)

    Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616(7956), 259–265 (2023)

    2023

  10. [18]

    Gao, C., et al.: Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis. Nat. Mach. Intell. 5, 294–308 (2023) https://doi.org/10.1038/s42256-023-00629-1

    2023 doi

  11. [19]

    Briefings in bioinformatics 23(6), 409 (2022)

    Luo, R., et al.: Biogpt: generative pre-trained transformer for biomedical text generation and mining. Briefings in bioinformatics 23(6), 409 (2022)

    2022

  12. [20]

    Advances in Neural Information Processing Systems 36 (2024)

    Li, C., et al.: Llava-med: Training a large language-and-vision assistant for 11 biomedicine in one day. Advances in Neural Information Processing Systems 36 (2024)

    2024

  13. [21]

    In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp

    Yildirim, N., et al.: Multimodal healthcare ai: identifying and designing clinically relevant vision-language applications for radiology. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 1–22 (2024)

    2024

  14. [22]

    arXiv preprint arXiv:2403.09827 (2024)

    Shen, Y., et al.: Fastsam3d: An efficient segment anything model for 3d volumetric medical images. arXiv preprint arXiv:2403.09827 (2024)

    2024 arXiv

  15. [23]

    In: Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications vol

    Kim, J.H., et al.: Development of a Smart Hospital Assistant: integrating arti- ficial intelligence and a voice-user interface for improved surgical outcomes. In: Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications vol...

    2021 doi

  16. [24]

    Killeen, B.D., et al.: In silico simulation: a key enabling technology for next- generation intelligent surgical systems. Prog. Biomed. Eng. 5(3), 032001 (2023) https://doi.org/10.1088/2516-1091/acd28b

    2023 doi

  17. [25]

    : DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures

    Unberath, M., et al. : DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pp. 98–106. Springer, Cham, Switzerland (2018). https://doi.org/10.1007/978-3-030-00937-3 12

    2018 doi

  18. [26]

    : Learning Transferable Visual Models From Natural Language Supervision

    Radford, A., et al. : Learning Transferable Visual Models From Natural Language Supervision. arXiv (2021) https://doi.org/10.48550/arXiv.2103.00020 2103.00020

    2021 doi arXiv

  19. [27]

    arXiv (2022) https://doi.org/10.48550/arXiv.2210.10163 2210.10163

    Wang, Z., et al.: MedCLIP: Contrastive Learning from Unpaired Medical Images and Text. arXiv (2022) https://doi.org/10.48550/arXiv.2210.10163 2210.10163

    2022 doi arXiv

  20. [28]

    In: 2021 IEEE 21st International Conference on Bioinformatics and Bioengineer- ing (BIBE), pp

    Opfermann, J.D., et al.: Feasibility of a cannula-mounted piezo robot for image- guided vertebral augmentation: Toward a low cost, semi-autonomous approach. In: 2021 IEEE 21st International Conference on Bioinformatics and Bioengineer- ing (BIBE), pp. 1–8 (2021). https://doi.o...

    2021

  21. [29]

    Radiology: Artificial Intelligence (2023)

    Wasserthal, J., et al.: TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence (2023)

    2023

  22. [30]

    In: International Conference on Text, Speech, and Dialogue, pp

    Demir, K.C., et al.: Pocap corpus: A multimodal dataset for smart operating room speech assistant using interventional radiology workflow analysis. In: International Conference on Text, Speech, and Dialogue, pp. 464–475 (2022). Springer

    2022

  23. [31]

    Vertebrae

    Killeen, B.D., et al.: Stand in surgeon’s shoes: virtual reality cross-training to enhance teamwork in surgery. Int. J. CARS 19(6), 1213–1222 (2024) https://doi. org/10.1007/s11548-024-03138-7 12 Appendix A Additional Results Prompt DICE Centroid Error (mm) “Vertebrae” 0.78 ± ...

    2024 doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.