ArticleSurgical endoscopy2025
Development of anatomical metrics for objective quantification of critical view of safety.
Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDebates on the subjective criteria for evaluating Surgical Milestones, such as achieving the critical view of safety (CVS) during cholecystectomy, remain a prominent focus and challenge in the field of surgical data science. In this study, we computed anatomical metrics with machine learning tools and investigated the relationship between these objective anatomical metrics and subjective criteria for CVS achievement.
methodsWe implemented and calibrated a zero-shot monocular depth estimation model for endoscopic images from cholecystectomies. These depth measures were integrated with human-annotated segmentation masks of three key anatomical structures relevant to CVS: cystic duct, cystic artery, and gallbladder. Computational geometry techniques were then employed to extract structure-specific depth distributions and compute two anatomical metrics: diagonal length and surface area. We tested for significant differences in these case-wise metrics, grouped by human-annotated CVS status.
results2256 frames (35 cases) were graded on CVS criteria, of which 343 frames (17 cases) met all three CVS criteria and 384 frames (17 cases) met no CVS criteria. The calibrated depth model achieves 0.063 on absolute relative error and 0.774 on squared relative error in the metric measurement. The diagonal length and surface area of both the cystic duct and cystic artery were significantly larger when all CVS criteria were met. On average, the cystic duct (cystic artery) length was 7.6 mm (13.6 mm) longer when CVS criteria was met.
conclusionIn this study, we presented a pipeline that generates anatomical measures from monocular endoscopic images utilizing a depth estimation model, anatomical segmentation masks, and computational geometry. The diagonal length and surface area of the cystic duct and cystic artery were found to be significantly larger in cases where all CVS criteria are met; this lends support to the use of these anatomical metrics as objective grounds for assessing CVS achievement.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.