Purpose: A videofluoroscopic swallowing study (VFSS) is the gold standard for the examination of swallowing function. A fluoroscopic unit and contrast medium are used to record an X-ray video of the patient's swallowing dynamics. This requires clinicians to observe three-dimensional swallowing movements on a two-dimensional video. In addition, the VFSS lacks facial surface information. In this study, we developed a method to synchronize the VFSS video with the three-dimensional movement of the facial surface. Approach: A 44-year-old man with no dysphagia was studied. Five smooth and one textured iron ball with a diameter of 3 mm were attached to the facial surface as markers at the tip of the nose, the upper and lower lips, the left and right corners of the mouth, and the chin. Using an X-ray fluoroscopic unit, the swallowing movements of gelatin jelly containing an iodine-based contrast media were recorded. The patient swallowed the jelly with mastication (right side, left side, both sides) and without mastication (open-mouth swallowing). At the same time, the movements of the facial surface were recorded using three video cameras. The four obtained videos were synchronized in terms of their start point, end point, and playback speed using a computer. Results: We created a synchronized video of the VFSS video and the three-dimensional video of the facial surface. This video could be observed from any viewpoint. In addition, we could analyze the velocity, distance, and angle of each point. Conclusion: This method can be used to objectively analyze mastication using numerical values.

Dental panoramic radiographs are often obtained at dental clinic visits for diagnosis and recording purposes. Automated filing of dental charts can help dentists in reducing their workload and improving diagnostic efficiency. The purpose of this study is to develop a system that prerecords a dental chart by recognizing teeth with their numbers and restoration history on dental panoramic radiographs. The proposed system uses YOLO which detects 16 types of teeth and restoration conditions simultaneously. Based on the detected tooth types, they were further classified into 32 types and combined with the tooth conditions by post-processing. We tested our method on 870 panoramic images obtained at 10 different facilities by 5-fold cross validation. The proposed method obtained 0.99 recall and precision for recognition of 32 tooth types and 0.90 recall and 0.90 precision on determining the tooth condition. It has the potential to assist prefiling the dental charts for efficient dental care.