A machine learning approach to predict treatment response in myofascial pain patients receiving masseter trigger point injections.

Authors

Alshaimaa Ahmed Shabaan, Islam Kassem, Aliaa Ibrahium Mahrous, Inass Aboulmagd, Islam A Amer, Ahmed Shaaban, Kareem Kamal Fathy, Reham Ragab, Mohamed Abd-El-Ghafour, Sally Ibrahim, Shaimaa Mohsen Refahee

Published in

BMC oral health. Volume 26. Issue 1. Aug 10, 2026. Epub Aug 10, 2026.

Abstract

Trigger point injection (TPI) therapy is widely used for masseter myofascial pain syndrome (MPS), yet outcomes vary substantially. Individualized prediction tools are lacking, often leading to trial-and-error treatment selection.
To develop, externally validate, and generalize ensemble machine learning models for predicting composite treatment success following masseter TPI, and to deploy a web-based clinical decision support system (CDSS).
This multicenter study included 1,181 patients with DC/TMD‑diagnosed masseter MPS treated with one of six injectable modalities. Baseline variables included pain (VAS), maximum mouth opening (MMO), and oral health‑related quality of life (OHIP-14). Composite treatment success was defined as simultaneous clinically meaningful improvement at 3 months: VAS reduction ≥ 2 points, MMO increase ≥ 5 mm, and OHIP-14 reduction ≥ 5 points. Random Forest (RF) and XGBoost models were trained on an internal cohort and externally validated on a geographically independent cohort. Performance was assessed using ROC‑AUC, precision‑recall AUC (PR‑AUC), calibration, decision curve analysis (DCA), and SHAP interpretability.
Overall composite success rate was 43.1%. Internal validation ROC‑AUC was 0.914 (RF) and 0.888 (XGBoost); external validation ROC‑AUC was 0.771 and 0.787, respectively. External PR‑AUC values were 0.759 (RF) and 0.761 (XGBoost). Both models showed good calibration and positive net benefit on DCA across clinically relevant thresholds. SHAP analysis identified baseline MMO, OHIP-14, age, pain intensity, and injectable modality as the most influential predictors, with consistent rankings across models and cohorts. The validated XGBoost model was deployed as a web‑based CDSS.
Machine learning models demonstrated good ability to predict multidimensional treatment success following masseter TPI. Baseline MMO, OHIP-14, age, pain intensity, and injectable modality were the strongest outcome determinants. External validation, SHAP interpretability, and DCA support model robustness and potential clinical utility for personalized treatment planning in MPS.
This tool may support treatment selection, reduce ineffective interventions, and improve patient outcomes in myofascial pain management.

PMID:
42572026
Bibliographic data and abstract were imported from PubMed on 10 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.