aceRepurposing a commercial artificial intelligence vision platform for automated surgical instrument identification: a proof-of-concept study
Background: Retained surgical items are classified as never events but continue to occur, and the count meant to prevent them is often recorded as correct even when an item is retained. Computer vision could provide an independent check on what is on the instrument table, but purpose-built surgical systems are scarce. Industrial vision platforms offer a faster route, although their surgical performance is unknown. Methods: In a prospective experimental study, a commercial artificial intelligence vision platform…
A repurposed industrial vision platform detected item presence in 99.1% of runs and identified exact item type in 92.5% of 2,200 bench experiments without software redesign.
A 7.5% residual identification error remained, concentrated in visually near-identical items such as the 6-0 suture needle (64% accuracy), and bench results do not show intraoperative performance.
The classifier was trained on this item set and tested under highly controlled conditions, so the figures are an upper bound; feasibility, not clinical benefit, is shown and an intraoperative study is still needed.
Evidence
- Peer-reviewedPatient Safety in Surgery2026-10-12
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Truvace Impact Record TRV-2026-1433, v1: “Repurposing a commercial artificial intelligence vision platform for automated surgical instrument identification: a proof-of-concept study.” Truvace, 2026-10-15. /record/TRV-2026-1433 (accessed at citation time). sha256 4e3b2a4400a565d4…
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