# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
## 1 Introduction  
Dysphagia can occur as a result of a wide variety of neurological, structural, or psychiatric disorders [1]. Dysphagia can lead to serious and life-threatening consequences, such as aspiration pneumonia or malnutrition, and has become a great concern in health care, especially among the aged population [2]. Comprehensive and objective evaluation of swallowing function is of vital importance for choosing appropriate measures to prevent or decrease the mortality and morbidity of patients with dysphagia [3]. The VFSS (videofluoroscopic swallowing study) procedure, also known as an MBS (modified barium swallowing) examination, is regarded as the gold standard for the evaluation of dysphagia and is the examination method that is most commonly used by clinicians for this purpose [3]. The VFSS is a radiographic procedure in which fluoroscopic images are acquired while the patient swallows boluses mixed with contrast media impenetrable to radiation beams [1]. The VFSS permits the direct visualization of bolus flow and the detection of the occurrence and timing of airway invasion, such as laryngeal penetration or aspiration, and it assists in identifying the physiological and often treatable causes of dysphagia [3]. Physicians and speech language pathologists usually review the recorded images, which are typically stored digitally as video files. However, the analysis of VFSS data is vulnerable to human bias because the swallowing process occurs in a very short time of less than a few seconds and includes several events occurring simultaneously, complicating the necessary judgments [4]. Intra-rater and inter-rater reliability have been reported to be only poor to fair(inter-rater k = 0.01\(\sim\)0.56) and show wide variation [5; 6; 7; 8]. The reviewing process frequently becomes time consuming and even exhausting because it requires repeated frame-by-frame or slow-motion examination [9]. As a result, VFSS analysis inevitably demands a high level of concentration and suffers from a considerable likelihood of error resulting from fatigue. To increase the efficiency and reliability by overcoming the human errors, attempts have been made to develop computer-assisted analysis methods. The suggested advantages of computerized reading are as follows: (1) more objective and immediate analysis, (2) elimination of the need for high levels of training, and (3) provision of a platform for larger scale screening [10]. [PERSON] et al. successfully reconstructed the three-dimensional pharyngeal bolus movement from the VHSvideos of normal subjects using a knowledge-based snake algorithm; however, its clinical usefulness has not been verified [11]. [PERSON] et al. measured the oral and pharyngeal transit time by demarcation of anatomical landmarks using the user-steered delineation algorithm live-wire and straight-line annotators; although they demonstrated a higher correlation coefficient than that between human observers, anatomical landmarks had to be demarcated by users [12]. [PERSON] et al. in another study attempted automatic demarcation of salient landmarks by applying a 16 point active shape model with high reliability; however, manual initialization was required to determine the co-ordinates of the reference points from the first frame of the video [10]. [PERSON] et al. attempted to track the motion of the hyoid bone semi-automatically by determining the region of interest using the Haar classifier, but manual identification of templates was required to increase accuracy [13]. [PERSON] et al. developed MATLAB-based software for kinematic analysis of swallowing with high correlation and reliability, but specific events and target structures should be marked manually [14]. Most existing models use automatic tracking of the salient anatomical structures following human manual demarcation in the first few frames.