Machine learning and data-driven models for predicting post-stroke dysphagia: a systematic review and meta-analysis

Post-stroke dysphagia (PSD) contributes to aspiration, pneumonia, malnutrition, prolonged hospitalization and mortality. We evaluated the discrimination, validity and readiness of machine learning and data-driven prediction models for PSD-related outcomes.

Methods

Following a prospectively registered protocol (PROSPERO CRD420261419259), we searched PubMed/MEDLINE, Embase, Web of Science Core Collection, CINAHL and CENTRAL from inception through June 7, 2026. Eligible studies developed or validated multivariable prediction models for PSD-related outcomes in adults with stroke. We used PROBAST and PROBAST+AI to assess risk of bias and applicability and TRIPOD+AI to evaluate reporting. Area under the curve (AUC) estimates were pooled on the logit scale with random-effects models.

Results

Twenty-four studies were included and ten contributed to meta-analysis. Four studies predicting early or incident PSD yielded a pooled AUC of 0.94 (95% CI 0.60–0.99; I² = 95.6%). Pooled AUCs were 0.84 (95% CI 0.71–0.92) for aspiration or penetration-aspiration and 0.89 (95% CI 0.24–1.00) for severe dysphagia. The exploratory analysis of all ten risk-prediction models produced an AUC of 0.90 (95% CI 0.80–0.95), but heterogeneity was substantial (I² = 90.3%) and the prediction interval was 0.51–0.99. Every study had high risk of bias because of analysis-domain concerns; calibration and external validation were uncommon.

Conclusions

Reported discrimination was often high, but the evidence does not establish reliable performance in care. Independent validation, calibration, complete model reporting and clinical-impact studies are needed before these models guide post-stroke swallowing care.