Quantitative Analysis of Instrument Motion Paths in Cataract Surgery across a Resident’s Training
Bibliographic record
Abstract
Purpose To objectively quantify the motion paths of surgical instruments during cataract surgery across a resident's training, identifying patterns of skill acquisition and proficiency development. Design An n = 1 panel study. Subjects One ophthalmology resident performing cataract surgery. Methods One hundred cataract surgery videos performed by a single resident from their sixth to 760th case were collected. Advanced motion tracking software (Computer Vision Annotation Tool) was utilized to annotate and track the trajectories of 11 surgical instruments on a frame-by-frame basis. Monotonic trends were assessed using the Mann–Kendall test and Theil–Sen slope estimation, with Spearman correlation measuring the association between case number and performance metric values. Pettitt change-point analysis identified significant transitions in the resident's skill progression. Main Outcome Measures Six key motion parameters, including total path length, average velocity, average acceleration, root mean square jerk, average angular change, and workspace coverage, were extracted for each instrument in each video. Results All 11 instruments demonstrated statistically significant reductions in ≥1 motion parameter. Path length consistently decreased across training, with the largest reductions seen in the cannula (–11.8%; 95% confidence interval [CI], –17.4% to –6.8%; P < 0.001), phacoemulsification handpiece (–11.5%; 95% CI, –14.1% to –8.7%; P < 0.001), and cystotome (–8.9%; 95% CI, –11.8% to –5.9%; P < 0.001). The intraocular lens inserter showed the greatest reduction in average angular change of 3.0% (–1.70°) (95% CI, –3.9% to –2.0%; P < 0.001). Pettitt analysis demonstrated significant shifts in surgical efficiency at around case 300 for most instruments, although improvements in certain advanced tasks (e.g., lens implantation) emerged later. Conclusions This large-scale, frame-by-frame motion tracking study revealed distinct instrument- and task-specific learning curves in cataract surgery, highlighting progressive changes in motion metrics over time. A significant shift at approximately case 300 marked a milestone in the resident's instrument use patterns. These findings underscore the potential of objective, video-based motion tracking analytics to provide data-driven resident feedback, guiding targeted instruction and standardizing cataract surgery training. Financial Disclosure(s) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".