Evaluating force and motion in posterior vitreous detachment manoeuvres using a robotic data acquisition system in cadaveric human eyes
Bibliographic record
Abstract
Deep vitrectomy, a complex ophthalmic procedure, demands precise instrument control and remains challenging to master. This study evaluated the ARASH:ASiST robotic system as a platform for real-time, quantitative assessment of deep vitrectomy in a pre-clinical setting using cadaveric human eyes. The system records force and motion data without disrupting the surgical workflow. Cadaveric eyes were prepared under strict safety protocols, and a mannequin head with a customised eye holder was designed, refined through repeated testing, and approved by surgeons to fully imitate real-world conditions. Four surgeons with different levels of experience, defined by their years of practice, performed the “drunk walk” manoeuvre while intraoperative force, positional, and temporal data were captured. Despite the inherent limitations of human cadaveric eyes, the aim was to reproduce the drunk walk method as a precise, predefined trajectory for posterior vitreous detachment induction. In total, 14 surgical trials were recorded: 2 from one expert surgeon (right-handed), 4 from two fellows (right-handed), and 8 from one intermediate surgeon, who performed four procedures with each hand. Quantitative metrics, including Normalised Jerk, Force Range, Peak Force Magnitude, and Force RMS, were used to compare individual performances. Group-level comparisons between expert, fellow, and intermediate surgeons were conducted using ANOVA and Kruskal–Wallis tests. While not statistically significant, likely due to the limited sample size, the analysis of descriptive statistics and effect sizes indicated potential trends in performance metrics across experience levels, highlighting the feasibility of the platform for capturing such variations. During data acquisition, the robotic system’s remote-centre-of-motion was accurately calibrated with laser-based technology, while encoders and force sensors were also precisely calibrated. The system’s real-time graphical interface provided immediate feedback and enabled detailed postoperative analysis. Usability testing confirmed its practicality and non-intrusiveness. Overall, the ARASH:ASiST system demonstrates feasibility as an objective platform for the evaluation of deep vitrectomy based on ex vivo results. These findings support further validation of this prototype in synthetic and clinical environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".