# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
## 4 Discussion  
In the present study, a deep learning model that can detect airway invasion from VFSS images without requiring any human effort was successfully developed. The entire procedure is accomplished in a fully automated manner, including image preprocessing, segmentation of the cervical spinal column, and finally, detection and reporting. As the deep learning network used in each module, we used U-Net developed for the segmentation of biomedical images and Xception module-based CNN for image classification. However, the specific type of deep learning network to be used for each module is optional. We used U-Net, which was developed for segmentation of biomedical image, for ROI determination, and other networks for segmentation [32; 33] can be \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy** & **Recall** & **Precision** & **Specificity** & **NPV** \\ \hline
93.2\% & 91.2\% & 88.1\% & 94.2\% & 95.8\% \\ (167/179) & (52/57) & (52/59) & (115/122) & (115/120) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance for classifying complete videos containing image frames with airway invasion. Figure 10: ROC curve for detecting airway invasion in an image frame. applied as well. Likewise, in addition to the Xception module-based CNN we used in this experiment, other networks [24; 34] for image classification can be used. Among the many variables in the VFSS, airway invasion (laryngeal penetration and aspiration) was selected as the target of model development because it has the greatest clinical significance. The occurrence of airway invasion corresponds to food materials entering the larynx and possibly proceeding down to the trachea and bronchial trees, potentially resulting in pulmonary morbidity or even mortality. The present model classifies image frames showing airway invasion with an overall accuracy of 97.2%. To the authors' knowledge, the fully automatic detection of any kind of abnormality from VFSS images has never previously been attempted. Most previous studies have used obsolete semiautomated identification and/or tracking algorithms [12; 13; 14; 15], in which salient landmarks must first be demarcated by humans. Two previous studies have attempted fully automated analysis of VFSS images, but did not address pathological findings. One of those studies investigated the accuracy of a deep learning model for the automatic identification of the hyoid bone [16]. The other study examined the utility of a deep learning model for the automatic detection of the pharyngeal phase, which is the main concern in swallowing evaluations [21]. Both studies reported considerably high accuracy, proving the usefulness of deep learning in VFSS analysis, but they did not address any pathological changes. One of the distinctive features of the present study is the creation of the \"dynamic\" ROI. A dynamic ROI is particularly important to avoid disturbances due to movements of the head and neck. During a VFSS examination, most patients will constantly move their head and neck to overcome their difficulties in swallowing. Identification and tracking of the boluses becomes difficult for both humans and machines when the position of the head and neck is changing among a set of VFSS images. A bolus travels from the oral cavity to the esophagus along a rather simple path that can be easily followed in VFSS images if the positions of the background structures are fixed. However, the passage of the bolus may appear irregular when the locations of the anatomical structures randomly change due to head and neck movements. To address this movement issue, a dynamic ROI generator was developed separately for application before airway invasion detection. The ROI is \"dynamic\" because its position moves in accordance with a detected reference point, which is chosen to be the center of the cervical spinal column. The cervical spinal column is located just behind the larynx and can be segmented using the U-Net segmentation network, with an overall accuracy of 99.0%.