Multimodal Prediction in Robotic Surgery: The Incremental Value of Task-Level Kinematic Data

Objective: To determine whether multimodal predictive models incorporating task-level objective performance indicators (OPIs) alongside preoperative clinical variables improve prediction of surgeon experience, case complexity, and outcomes in robotic cholecystectomy. Background: OPIs from robotic platforms enable automated assessment of intraoperative performance, but prior multimodal analyses have suggested limited incremental value beyond clinical variables. Whether this conclusion holds when OPIs are computed at task level and target specifically curated endpoints remains untested. Methods: We retrospectively analyzed 234 Calot’s triangle dissections, 198 by attending surgeons and 36 by trainees. Three feature sets per target—clinical-only (10 preoperative variables), OPI-only (10 target-specific kinematic features), and combined—were evaluated using nested 5-seed×5-fold cross-validation across 5 algorithms (logistic regression, support vector machine, random forest, XGBoost, LightGBM). Discriminative performance was compared by paired Wilcoxon signed-rank tests on fold-level area under the receiver operating characteristic curve (AUROC). Feature contributions were quantified with SHapley Additive exPlanations (SHAP) analysis. Results: For surgeon experience and case complexity, OPIs alone matched the combined model (AUROC: 0.967 vs. 0.964; 0.791 vs. 0.792; both P >0.05) and outperformed clinical variables alone ( P <0.001 and P =0.006). For outcomes, the combined model significantly exceeded both single-modality models (AUROC: 0.753 vs. 0.720 clinical and 0.720 OPI-only; P =0.015 and 0.005). OPIs contributed 90.7%, 73.3%, and 51.7% of total absolute SHAP value across the 3 targets, decreasing with distance from intraoperative kinematics. Conclusions: Task-level, target-specific OPIs carry independent discriminative information beyond routine preoperative clinical variables. Their incremental value is target-dependent, with multimodal integration offering statistically significant but modest gains for outcomes.