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Updated: Jun 26, 2025
Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
Published on: March 1, 2015
Clinical evaluation of a machine learning-based dysphagia risk prediction tool
Markus Gugatschka1, Nina Maria Egger2, K Haspl2
1Department of Phoniatrics, ENT University Hospital Graz, Medical University Graz, Graz, Austria. markus.gugatschka@medunigraz.at.
Purpose:
The rise of digitization promotes the development of screening and decision support tools. We sought to validate the results from a machine learning based dysphagia risk prediction tool with clinical evaluation.
Methods:
149 inpatients in the ENT department were evaluated in real time by the risk prediction tool, as well as clinically over a 3-week period. Patients were classified by both as patients at risk/no risk.
Results:
The AUROC, reflecting the discrimination capability of the algorithm, was 0.97. The accuracy achieved 92.6% given an excellent specificity as well as sensitivity of 98% and 82.4% resp. Higher age, as well as male sex and the diagnosis of oropharyngeal malignancies were found more often in patients at risk of dysphagia.
Conclusion:
The proposed dysphagia risk prediction tool proved to have an outstanding performance in discriminating risk from no risk patients in a prospective clinical setting. It is likely to be particularly useful in settings where there is a lower incidence of patients with dysphagia and less awareness among staff.
