Diagnostic value of a deep learning-based hyoid bone tracking model for aspiration in patients with post-stroke dysphagia

Abstract

Objective

Hyoid bone movement is potentially related to aspiration risk in post-stroke dysphagia (PSD) patients but is difficult to assess quantitatively. This study aimed to measure the distance of hyoid bone movement more efficiently and accurately using a deep learning model and determine the clinical usefulness of the model in PSD patients.

Methods

This study included 85 patients with PSD within 6 months from onset. Patients were grouped into an aspiration group ( n = 35) and a non-aspiration group ( n = 50) according to the results of a videofluoroscopic swallowing study. Hyoid bone movement was tracked using a deep learning model constructed with the BiFPN-U-Net(T) architecture. The maximum distance of hyoid bone movement was measured horizontally ( H _max), vertically ( V _max), and diagonally ( D _max).

Results

Compared with the non-aspiration group, the aspiration group showed significant decreases in hyoid bone movement in all directions. The area under the curve of V _max was highest at 0.715 with a sensitivity of 0.680 and specificity of 0.743. The V _max cutoff value for predicting aspiration risk was 1.61 cm. The success of oral feeding at the time of discharge was significantly more frequent when hyoid movement was equal to or larger than the cutoff value although no significant relationship was found between hyoid movement and other clinical characteristics.

Conclusion

Hyoid bone movement of PSD patients can be measured quantitatively and efficiently using a deep learning model. Deep learning model-based analysis of hyoid bone movement seems to be useful for predicting aspiration risk and the possibility of resuming oral feeding.