Anticipating the Next Surgical Item in Oral Surgery to Support Scrub Nurses: Deep Learning Using Event History and Contextual Information

Anticipating surgical instruments and consumables can enhance scrub nurses’ preparation. We developed next-item anticipation models using event history and contextual information, excluding surgical field video input. This single-center study analyzed 5391 provision or self-retrieval events prospectively recorded from preparation to completion of 34 oral and maxillofacial procedures involving horizontally impacted third molar extraction. Markov, gated recurrent unit (GRU), and Transformer models were evaluated using nested leave-one-case-out cross-validation with inner five-fold model selection. The primary outcome was case-averaged Top-5 accuracy across 62 individual item classes. Postoperatively assessed extraction difficulty was also examined in a sensitivity analysis. Candidate constraints excluded unavailable items based on prepared quantities and intraoperative use. History-only single-seed GRU and Transformer models exceeded Markov in mean case-averaged Top-5 accuracy across three seeds by 8.2 and 6.7 percentage points, respectively. Before the constraints, the Top-5 accuracy was 60.4% for Markov, 70.2% for context-informed GRU ensembles, and 69.3% for context-informed Transformer ensembles. Context selection and re-tuning yielded small increases in point estimates. Constraints improved accuracy by approximately two percentage points in all three models. These findings support developing candidate presentation systems to assist scrub nurses’ preparation. These internal results require multicenter validation and in-use evaluation to establish generalizability and clinical utility.