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REVIEW 4 major objections 5 minor 25 references

A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A deep-learning-driven robot performs the full retinal vein cannulation pipeline in a live chicken embryo model, detecting punctures with 85% accuracy.

desk verdict A real integrated pipeline for autonomous retinal vein cannulation on chicken embryos, but the headline 85% accuracy is a classifier metric rather than a demonstrated procedural success rate. read the letter →

arxiv 2507.21965 v1 pith:43ZBZBU4 submitted 2025-07-29 cs.RO

Yi Wang , Peiyao Zhang , Mojtaba Esfandiari , Peter Gehlbach , Iulian I. Iordachita This is my paper · ORCID
classification cs.RO
keywords retinalveincannulationautonomousmicrosurgeryopticalcoherencetomographydeeplearningchickenembryomodelsurgicalroboticspuncturedetectionneedlenavigation
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

Retinal vein cannulation is a microsurgical procedure for retinal vein occlusion that is hard to perform by hand because veins are narrow, tissue moves, and surgeons' tremor exceeds the vein diameter. This paper tries to establish that a robot can automate the whole procedure end to end: navigate a needle to a target vein, detect contact, puncture the vein, and verify the puncture, all using microscope images for lateral position and B-scan OCT cross-sections for depth. The authors report 85% puncture-detection accuracy on 20 live chicken embryos and average autonomous navigation and puncture times of 36.74 s and 26.97 s, much faster than keyboard-teleoperation baselines of 117 s and 469 s. If the result holds, it suggests that deep-learning-driven visual servoing plus OCT depth sensing could turn retinal vein cannulation from a tremor-limited manual skill into a repeatable automated procedure.

What carries the argument

The load-bearing mechanism is the pairing of B-scan OCT depth information with three trained vision models. A keypoint-detection network on microscope images locates the needle tip and computes a velocity command toward the target. A binary contact classifier on B-scan cross-sections decides whether the needle has reached the vein wall, and if not, the robot increments downward. A puncture detector on B-scans labels each attempt as success or failure and triggers a retract-and-retry loop when the puncture is not confirmed; the retry retracts by two-fifths of the puncture depth before reattempting. This closed loop of optical sensing, learned classification, and robot motion is what makes the procedure autonomous once the surgeon selects the target.

What would settle it

Deliberately offset the OCT B-scan plane from the needle tip by about one needle diameter during autonomous punctures and measure puncture-detection accuracy; if accuracy does not collapse, the claim that B-scan depth information drives the decision is not supported. A second check is to compare the robot's commanded depth against the OCT-measured needle-tip depth across trials, a quantity the paper does not report.

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Extended reading notes

Core claim

The central discovery is that the full retinal vein cannulation sequence can be executed autonomously by coupling two image sources with three learned decisions. A microscope image gives the lateral needle-tip position for navigation; a B-scan OCT cross-section gives the depth information needed to judge when the needle touches the vein wall and when the wall has been punctured. The system first drives the needle to a user-selected 2D target, then lowers it until a binary contact classifier confirms contact, then advances at higher speed and uses a puncture detector to confirm success, retracting and retrying when the puncture is not confirmed. On 20 chicken embryos it classified puncture outcomes with 85% accuracy, with an F1 score of 0.87 for successful punctures, and completed navigation and puncture in 36.74 s and 26.97 s on average.

Load-bearing premise

The system assumes the B-scan OCT cross-section shows the actual needle-to-vein interaction; if the scan plane drifts or lighting and tissue motion hide the needle, the contact and puncture classifiers are deciding on images that do not show what they are supposed to classify.

Editorial extensions

If this is right

  • An operator's only input is a 2D target on the microscope image; the robot then handles navigation, contact, puncture, and verification without manual intervention.
  • Autonomous navigation averaged 36.74 s compared with 117 s for keyboard teleoperation, a 68.6% reduction.
  • Autonomous puncture averaged 26.97 s compared with 469 s for keyboard teleoperation, roughly a 17-fold reduction.
  • Puncture classification reached 85% accuracy, with precision 0.90/0.82 and recall 0.75/0.93 for failure/success classes.
  • Failures came from lighting variation, tissue motion, and needle occlusion degrading B-scan contrast, so the paper identifies robustness to these conditions as necessary next work.

Reading between the lines

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

  • If the 85% accuracy transfers to smaller vessels, adding a confidence threshold before puncturing could reduce the misclassified failure cases the model currently produces.
  • The speed comparison is against keyboard teleoperation, not against a surgeon's hands; a fair clinical comparison would include setup and decision time and a tremor model.
  • Because chicken embryo veins are roughly an order of magnitude wider than human retinal veins, the contact and puncture classifiers would likely need retraining on smaller-caliber vessels before human use, a transfer the paper lists as future work.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper presents an automated robotic system for retinal vein cannulation (RVC) that combines a top-down microscope with B-scan optical coherence tomography (OCT) and deep-learning modules for needle tip navigation, contact detection, and puncture recognition. Validation is carried out on a chicken embryo model, where the system reports 85% classification accuracy for puncture detection, reduced navigation and puncture times compared to manual (keyboard) control, and an independent outcome check by air injection into the vein. The authors acknowledge limitations, including sensitivity to lighting and occlusion, lack of depth-control accuracy quantification, and absence of a systematic failure-mode analysis. The central feasibility claim is that deep learning plus B-scan OCT can support real-time navigation, contact detection, and puncture verification for a microsurgical task in a living animal model.

Significance. If the result holds, the system is a meaningful step toward automating a delicate microsurgical procedure; the use of an independent physical outcome check (air-induced vein inflation or blood exit) is a strength, as is the integration of two robotic platforms with real-time OCT imaging. However, the evidence presented is currently at the level of a per-attempt binary classifier evaluated on 27 attempts, without a per-procedure success rate, confidence interval, or quantitative depth-accuracy metric. The feasibility insight is real but the full-pipeline autonomy claim is not yet supported by the reported metrics. The paper also offers a useful comparison with prior robotic RVC work and is transparent about several limitations.

major comments (4)
  1. [Section V, Table II] The headline 85% accuracy is a per-attempt classification accuracy on 27 puncture attempts, not a procedural cannulation success rate. The precision and recall values imply a confusion matrix of roughly TN=9, FP=3, FN=1, TP=14. Because the control policy in Section III stops and fully retracts the needle once the model predicts puncture, a false-positive prediction would terminate the procedure without actual cannulation. The paper does not report how many of the 20 embryos were ultimately confirmed by the air-injection check to have been successfully cannulated by the autonomous pipeline. Please report the per-procedure success rate, the number of attempts terminated by false positives, and a confidence interval for the per-attempt accuracy.
  2. [Section V, Table I] The timing comparison (automated puncture time 26.97 s vs manual 469 s) is not clearly defined: it is unclear whether the manual baseline includes the same stages (contact detection, puncture, verification) and from which event each timing measurement starts and ends. The navigation time reduction (36.74 s vs 117 s) is also reported without the corresponding navigation accuracy, such as the distribution of final pixel-distance errors relative to the 3-pixel threshold. Time savings alone are not evidence of improved overall performance unless the success rates of the automated and manual conditions are also reported.
  3. [Section VI and Section III] The conclusion states that 'we did not quantify the needle's depth control accuracy nor perform a systematic analysis of failure modes.' This is load-bearing because the abstract claims 'precise depth sensing' via B-scan OCT, and both contact detection and puncture recognition rely on B-scan images that must capture the needle-tissue interaction in the imaging plane. Without reporting a metric for B-scan plane alignment, needle tip localization error in depth, or z-axis positioning error, the 'precise depth sensing' claim is unsupported. The paper should provide at least one quantitative measure of depth positioning accuracy or an explicit characterization of the B-scan plane registration error.
  4. [Section IV-C and Table II] The validation description says the procedure was 'conducted across 20 embryos,' but Table II reports 27 puncture attempts (support of 12 for Class 0 and 15 for Class 1). The relationship between the 20 embryos and the 27 attempts is not explained (e.g., how many retries occurred and whether retries were counted as independent samples). This ambiguity affects the interpretation of the reported accuracy, the validity of any statistical inference, and the reader's ability to assess whether the three false positives correspond to three distinct embryos or repeated attempts on the same embryo.
minor comments (5)
  1. [Section V] There is a typo in the sentence 'improved puncture time to an average of 26.97 s,significantly faster' — the comma should be followed by a space before 'significantly.'
  2. [Section II-D] The dataset notation D = {(Ii, bi)}N i=1 is malformed; it should be typeset as D = {(I_i, b_i)}_{i=1}^{N} for clarity.
  3. [Section III-A and Figure 3] The navigation model is described as 'a ResNet-101 backbone with a key point RCNN head' but Figure 3(a) labels the model as 'Mask RCNN'; these names should be reconciled.
  4. [Section V] The comparison to Willekens et al.'s 73% procedural success rate is apples-to-oranges because that metric is a per-procedure cannulation success, whereas the 85% here is a per-attempt classification accuracy; the comparison should be clearly framed as non-equivalent or replaced with a procedural success rate.
  5. [Table I and Table II] No confidence intervals or statistical tests are provided for the time comparisons or the classification metrics; reporting 95% confidence intervals for the accuracy and the time differences would help assess precision given the small sample sizes.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the system's claims are validated against external physical outcomes, not against its own fitted outputs.

full rationale

This paper is an empirical systems-and-validation study rather than a derivation, so the circularity failure modes enumerated in the review criteria do not arise. The central performance claims—85% puncture detection accuracy, navigation time, and puncture time—are measured against external ground truth: the paper states that puncture success was confirmed by 'observing visible inflation of the vein and blood exiting from the puncture site' (Section IV-C), and cases without inflation or blood were marked as failures. The navigation, contact, and puncture classifiers are trained on expert-annotated images and then evaluated on live chicken embryos; no fitted parameter is renamed as a prediction, and no equation reduces to its own input. The paper does cite prior work by the same group (EyeRobot 2.0 [7], EyeRobot 2.1 [25], and the keyboard-teleoperation workflow [21]), but these citations support hardware infrastructure and prior feasibility, not the novel autonomous-integration claim, which is independently tested in 20 embryos. The paper's own stated limitations—lack of quantified depth-control accuracy, sensitivity to lighting and occlusion, and domain-shift concerns—are validity and generalizability caveats, not evidence of circularity. Therefore, no circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. The central claims rest on surrogate-model validity, imaging-plane alignment, annotation reliability, and several hand-set thresholds (three of which are not quantified in the text). The navigation stop threshold is stated; contact and puncture confidence thresholds are not.

free parameters (4)
  • Navigation stop threshold = 3 pixels (0.0586 mm at ROI)
    Used to define when navigation succeeds; described as empirically set in Section III-A.
  • Contact classification probability threshold = not reported
    Paper mentions a predefined threshold for contact class probability but never gives its value; the threshold directly determines contact decisions.
  • Puncture retry retraction distance = 2/5 of puncture depth
    Section III: needle retracts by 2/5 of puncture depth before reattempting, 'a strategy based on experiments'.
  • Puncture confidence threshold = not reported
    Section III-C: 'If no puncture is detected with high confidence' implies a threshold that is not specified.
assumptions (5)
  • domain assumption The chicken embryo model is a valid surrogate for human retinal vein cannulation because of anatomical similarity and transparent vasculature.
    Invoked in Section II-A with citation [22]; no validation against human anatomy in this paper, and the conclusion notes chicken embryo veins are larger than human retinal veins.
  • domain assumption The B-scan OCT image plane captures the needle-vein interaction in the region of interest.
    Section III relies on B-scan images for contact and puncture detection; if the plane is misaligned or motion-degraded, the classifiers see the wrong cross-section.
  • domain assumption Expert annotation of the first sustained visual overlap between needle and vessel wall is correct ground truth for contact.
    Section III-B: ground truth contact labels were manually annotated by expert observation; no inter-rater agreement or independent verification reported.
  • domain assumption Visible vein inflation or blood exiting the puncture site is a valid confirmation that puncture occurred.
    Section IV-C defines success this way; it is a reasonable physical check but is a manual visual judgment, not a quantitative measurement.
  • domain assumption Manual leveling of the robot ensures the XOY task plane aligns with the microscope image plane.
    Section III-A: navigation assumes this alignment so that pixel distances map to planar robot motion; misalignment would bias navigation.

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Cite this review

Pith. "Pith review of A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model." pith.science (2026). https://pith.science/paper/43ZBZBU4

@misc{pith2026250721965,
  author       = {Pith},
  title        = {Pith review of: A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/43ZBZBU4}},
  note         = {Machine review of arXiv:2507.21965}
}
read the original abstract

Retinal vein cannulation (RVC) is a minimally invasive microsurgical procedure for treating retinal vein occlusion (RVO), a leading cause of vision impairment. However, the small size and fragility of retinal veins, coupled with the need for high-precision, tremor-free needle manipulation, create significant technical challenges. These limitations highlight the need for robotic assistance to improve accuracy and stability. This study presents an automated robotic system with a top-down microscope and B-scan optical coherence tomography (OCT) imaging for precise depth sensing. Deep learning-based models enable real-time needle navigation, contact detection, and vein puncture recognition, using a chicken embryo model as a surrogate for human retinal veins. The system autonomously detects needle position and puncture events with 85% accuracy. The experiments demonstrate notable reductions in navigation and puncture times compared to manual methods. Our results demonstrate the potential of integrating advanced imaging and deep learning to automate microsurgical tasks, providing a pathway for safer and more reliable RVC procedures with enhanced precision and reproducibility.

Figures

Figures reproduced from arXiv: 2507.21965 by the authors.

Figure 1
Figure 1. Experimental setup and key components of the autonomous system [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Block diagram of the proposed workflow. The process is divided [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Training results of navigation model and puncture detection model: total loss (a) of key point detection model (Detectron2 - Mask RCNN), box [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Representative examples from the validation experiments for retinal [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 4
Figure 4. Figure 4: Exemplary results of every module and the final validation result. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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Reference graph

Works this paper leans on

25 extracted references · 24 canonical work pages

  1. [1]

    The royal college of ophthalmologists guidelines on retinal vein occlusions: executive sum- mary,

    S. Sivaprasad, W. Amoaku, and P. Hykin, “The royal college of ophthalmologists guidelines on retinal vein occlusions: executive sum- mary,” Eye, vol. 29, no. 12, pp. 1633–1638, 2015

    work page 2015

  2. [2]

    Global epidemiology of retinal vein occlusion: a systematic review and meta-analysis of prevalence, incidence, and risk factors,

    P. Song, Y . Xu, M. Zha, Y . Zhang, and I. Rudan, “Global epidemiology of retinal vein occlusion: a systematic review and meta-analysis of prevalence, incidence, and risk factors,” Journal of global health , vol. 9, no. 1, 2019

    work page 2019

  3. [3]

    Retinal vein cannulation with prolonged infusion of tissue plasminogen activator (t-pa) for the treatment of experimental retinal vein occlusion in dogs,

    M. K. Tameesh, R. R. Lakhanpal, G. Y . Fujii, M. Javaheri, T. H. Shelley, S. D’anna, A. C. Barnes, E. Margalit, M. Farah, E. De Juan Jr et al. , “Retinal vein cannulation with prolonged infusion of tissue plasminogen activator (t-pa) for the treatment of experimental retinal vein occlusion in dogs,” American journal of ophthalmology, vol. 138, no. 5, pp. ...

    work page 2004

  4. [4]

    A study of instrument motion in retinal microsurgery,

    C. N. Riviere and P. S. Jensen, “A study of instrument motion in retinal microsurgery,” in Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No. 00CH37143) , vol. 1. IEEE, 2000, pp. 59–60

    work page 2000

  5. [5]

    Di- ameters of retinal blood vessels in a healthy cohort as measured by spectral domain optical coherence tomography,

    D. Goldenberg, J. Shahar, A. Loewenstein, and M. Goldstein, “Di- ameters of retinal blood vessels in a healthy cohort as measured by spectral domain optical coherence tomography,” Retina, vol. 33, no. 9, pp. 1888–1894, 2013

    work page 2013

  6. [6]

    Robot-assisted retinal vein cannulation in an in vivo porcine retinal vein occlusion model,

    K. Willekens, A. Gijbels, L. Schoevaerdts, L. Esteveny, T. Janssens, B. Jonckx, J. H. Feyen, C. Meers, D. Reynaerts, E. Vander Poorten et al. , “Robot-assisted retinal vein cannulation in an in vivo porcine retinal vein occlusion model,” Acta ophthalmologica , vol. 95, no. 3, pp. 270–275, 2017

    work page 2017

  7. [7]

    New steady-hand eye robot with micro-force sensing for vitreoretinal surgery,

    A. ¨Uneri, M. A. Balicki, J. Handa, P. Gehlbach, R. H. Taylor, and I. Iordachita, “New steady-hand eye robot with micro-force sensing for vitreoretinal surgery,” in 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics . IEEE, 2010, pp. 814–819

    work page 2010

  8. [8]

    Cooperative vs. teleop- eration control of the steady hand eye robot with adaptive sclera force control: A comparative study,

    M. Esfandiari, J. W. Kim, B. Zhao, G. Amirkhani, M. Hadi, P. Gehlbach, R. H. Taylor, and I. Iordachita, “Cooperative vs. teleop- eration control of the steady hand eye robot with adaptive sclera force control: A comparative study,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 8209–8215

    work page 2024

Show all 25 references
  1. [9]

    Robot assistance for micrometer precision in vitreoretinal surgery,

    T. Meenink, G. Naus, M. de Smet, M. Beelen, and M. Steinbuch, “Robot assistance for micrometer precision in vitreoretinal surgery,” Investigative ophthalmology & visual science , vol. 54, no. 15, pp. 5808–5808, 2013

    2013

  2. [10]

    The introduction of a new robot for assistance in ophthalmic surgery,

    M. A. Nasseri, M. Eder, S. Nair, E. Dean, M. Maier, D. Zapp, C. P. Lohmann, and A. Knoll, “The introduction of a new robot for assistance in ophthalmic surgery,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, ...

    2013

  3. [11]

    Design and realisation of a novel robotic manipulator for retinal surgery,

    A. Gijbels, N. Wouters, P. Stalmans, H. Van Brussel, D. Reynaerts, and E. Vander Poorten, “Design and realisation of a novel robotic manipulator for retinal surgery,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2013, pp. 3598– 3603

    2013

  4. [12]

    Robotic retinal surgery,

    E. Vander Poorten, C. N. Riviere, J. J. Abbott, C. Bergeles, M. A. Nasseri, J. U. Kang, R. Sznitman, K. Faridpooya, and I. Iordachita, “Robotic retinal surgery,” in Handbook of robotic and image-guided surgery. Elsevier, 2020, pp. 627–672

    2020

  5. [13]

    To- wards autonomous eye surgery by combining deep imitation learning with optimal control,

    J. W. Kim, P. Zhang, P. Gehlbach, I. Iordachita, and M. Kobilarov, “To- wards autonomous eye surgery by combining deep imitation learning with optimal control,” in Conference on Robot Learning . PMLR, 2021, pp. 2347–2358

    2021

  6. [14]

    Towards safer retinal surgery through chance constraint optimization and real-time geometry estimation,

    P. Zhang, J. W. Kim, and M. Kobilarov, “Towards safer retinal surgery through chance constraint optimization and real-time geometry estimation,” in 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021, pp. 5175–5180

    2021

  7. [15]

    Autonomous needle navigation in retinal microsurgery: Evaluation in ex vivo porcine eyes,

    P. Zhang, J. W. Kim, P. Gehlbach, I. Iordachita, and M. Kobilarov, “Autonomous needle navigation in retinal microsurgery: Evaluation in ex vivo porcine eyes,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 4661–4667

    2023

  8. [16]

    Towards autonomous retinal microsurgery using rgb-d images,

    J. W. Kim, S. Wei, P. Zhang, P. Gehlbach, J. U. Kang, I. Iordachita, and M. Kobilarov, “Towards autonomous retinal microsurgery using rgb-d images,” IEEE Robotics and Automation Letters , 2024

    2024

  9. [17]

    Autonomous needle navigation in subretinal injections via ioct,

    P. Zhang, J. W. Kim, P. Gehlbach, I. Iordachita, and M. Kobilarov, “Autonomous needle navigation in subretinal injections via ioct,”IEEE Robotics and Automation Letters , 2024

    2024

  10. [18]

    Real-time deformation-aware control for autonomous robotic subreti- nal injection under ioct guidance,

    D. Arikan, P. Zhang, M. Sommersperger, S. Dehghani, M. Esfandiari, R. H. Taylor, M. A. Nasseri, P. Gehlbach, N. Navab, and I. Iordachita, “Real-time deformation-aware control for autonomous robotic subreti- nal injection under ioct guidance,” arXiv preprint arXiv:2411.06557 , 2024

    2024 arXiv

  11. [19]

    Towards motion compensation in autonomous robotic subretinal injections,

    D. Arikan, M. Esfandiari, P. Zhang, M. Sommersperger, S. Dehghani, R. H. Taylor, M. A. Nasseri, P. Gehlbach, N. Navab, and I. Iordachita, “Towards motion compensation in autonomous robotic subretinal injections,” in 2025 International Symposium on Medical Robotics (ISMR). IEEE...

    2025

  12. [20]

    Deep learning-enhanced robotic subretinal injection with real- time retinal motion compensation,

    T. Wu, M. Esfandiari, P. Zhang, R. H. Taylor, P. Gehlbach, and I. Iorda- chita, “Deep learning-enhanced robotic subretinal injection with real- time retinal motion compensation,” arXiv preprint arXiv:2504.03939 , 2025

    2025 arXiv

  13. [21]

    A feasible workflow for retinal vein cannulation in ex vivo porcine eyes with robotic assistance,

    P. Zhang, P. Gehlbach, M. Kobilarov, and I. Iordachita, “A feasible workflow for retinal vein cannulation in ex vivo porcine eyes with robotic assistance,” in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) . IEEE, 2024

    2024

  14. [22]

    A comparison of manual and robot-assisted retinal vein cannulation in chicken chorioallantoic membrane,

    N. Patel, M. Urias, C. He, P. L. Gehlbach, and I. Iordachita, “A comparison of manual and robot-assisted retinal vein cannulation in chicken chorioallantoic membrane,” in 2020 42nd Annual Interna- tional Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), ...

    2020

  15. [23]

    Intraoperative spec- tral domain optical coherence tomography for vitreoretinal surgery,

    Y . K. Tao, J. P. Ehlers, C. A. Toth, and J. A. Izatt, “Intraoperative spec- tral domain optical coherence tomography for vitreoretinal surgery,” Optics letters, vol. 35, no. 20, pp. 3315–3317, 2010

    2010

  16. [24]

    Effects of dataset size and interactions on the prediction per- formance of logistic regression and deep learning models,

    A. Bailly, C. Blanc, ´E. Francis, T. Guillotin, F. Jamal, B. Wakim, and P. Roy, “Effects of dataset size and interactions on the prediction per- formance of logistic regression and deep learning models,” Computer Methods and Programs in Biomedicine , vol. 213, p. 106504, 2022

    2022

  17. [25]

    Toward clinically applicable steady-hand eye robot for vitreoretinal surgery,

    X. He, D. Roppenecker, D. Gierlach, M. Balicki, K. Olds, P. Gehlbach, J. Handa, R. Taylor, and I. Iordachita, “Toward clinically applicable steady-hand eye robot for vitreoretinal surgery,” in ASME Interna- tional Mechanical Engineering Congress and Exposition , vol. 45189. Am...

    2012

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