Letter to the editor: interpretable machine learning model predicts 1‑year inguinal hernia risk after robot‑assisted radical prostatectomy.

Abstract

We read with interest the recent article by Yu et al., "Interpretable machine learning model predicts 1-year inguinal hernia risk after robot-assisted radical prostatectomy" (DOI: 10.1007/s11701-025-02723-5) , which represents an important step in applying explainable machine learning to postoperative complication prediction. The authors should be commended for highlighting inguinal hernia as an underrecognized sequela of robot-assisted radical prostatectomy and for pioneering the use of SHAP-based interpretation in this context. Their findings offer valuable groundwork for risk stratification and personalized counseling. While the study acknowledges several limitations, we wish to highlight additional concerns that warrant consideration. First, the reliance on a single, ethnically homogeneous cohort may limit generalizability across diverse populations. Second, omission of intraoperative surgical technique variables-such as Retzius-sparing approaches, peritoneal closure, or extraperitoneal access-restricts the model's ability to account for modifiable surgical factors. Third, the use of symptom-driven ultrasonography risks underdetection of subclinical hernias, introducing potential bias. Fourth, the one-year follow-up period may underestimate true incidence, as many cases manifest within 2-3 years postoperatively. Finally, the feature set was confined to five predictors, overlooking biological and functional variables such as collagen metabolism, frailty indices, and continence recovery, which are known to influence hernia development.We suggest that future research incorporate multicenter, ethnically diverse cohorts, longer follow-up, standardized imaging, and expanded biological and surgical predictors. These steps will enhance predictive accuracy, clinical utility, and generalizability. Our critique aims to complement the authors' contribution and foster refinement of machine learning models for postoperative complication risk prediction.