Multimodal Prediction in Robotic Surgery: The Incremental Value of Task-Level Kinematic Data
Mattia Ballo, Ilaria Billato, Aditya Amit Godbole, Emaad Iqbal, Maria Clara Morais, Ruben Parra Salas, Alessandro Furlanetto, Matteo Pittacolo, Elizabeth W. Tindal, Jeffrey Nussbaum, Andrew Yee, Anthony Jarc, Filippo FilicoriObjective:
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
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.