Toward the Art of Robotic-assisted Vitreoretinal Surgery.
Bibliographic record
Abstract
New technological progress in robotics has brought many beneficial clinical applications. Currently, computer integrated robotic surgery has gained clinical acceptance for several surgical procedures. Robotically assisted eye surgery is envisaged as a promising solution to overcome the shortcomings inherent to conventional surgical procedures as in vitreoretinal surgeries. Robotics by its high precision and fine mechanical control can improve dexterity, cancel tremor, and allow highly precise remote surgical capability, delicate vitreoretinal manipulation capabilities. Combined with magnified three-dimensional imaging of the surgical site, it can enhance surgical precision. Tele-manipulation can provide the ability for tele-surgery or haptic feedback of forces generated by the manipulation of intraocular tissues. It presents new solutions for some sight-threatening conditions such as retinal vein cannulation where, due to physiological limitations of the surgeon's hand, the procedure cannot be adequately performed. In this paper, we provide an overview of the research and advances in robotically assisted vitreoretinal eye surgery. Additionally the barriers to the integration of this method in the field of ocular surgery are summarized. Finally, we discuss the possible applications of the method in the area of vitreoretinal surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".