Development of Dysphagia Evaluation Via Video Analysis Based on Deep Learning Method in Neonates and Infants and Correlation Between the Evaluation and the Development

Although the exact frequency of dysphagia in newborns is not known, according to a paper published by Motion et al. in 2001, the prevalence of eating problems in premature infants under 37 weeks of age was 10.5%, and in 2001, Mercado-Deane et al. reported that about 26% had dysphagia. In 1996, Reilly et al. reported that more than 90% of children with polio had oral movement disorders and 38% had dysphagia. It is known that the frequency of dysphagia in newborns and infants is not low. The risk factors that cause these dysphagia are very diverse, and dysphagia in children can be induced by causes that can affect the whole process of swallowing.

The evaluation of dysphagia can be divided into an evaluation method that does not use an instrument such as SOMA, SDS, and quality of life measurement, and an evaluation method that uses an instrument such as VFSS and FEES. However, it is difficult to conduct tests such as VFSS in newborns and infants due to poor coordination, and there is also a risk of radiation exposure. In addition, there are practical difficulties in applying the evaluation in all medical institutions because special facilities are required to implement VFSS and specialized clinical personnel are required.

The Neonatal Oral-Motor Assessment Scale (NOMAS), developed by Marjorie Meyer Palmer in 1983, is an evaluation method for dysphagia applicable to infants under 48 weeks of PMA that evaluates whether there are abnormal findings by observing sucking for 5 minutes. However, NOMAS has the disadvantage that a person has to directly observe the sucking and may show differences in results between raters.

Therefore, in this study, inspired by the evaluation method of NOMAS, investigators try to develop an evaluation method to evaluate swallowing disorders by videotaping bottle feeding in newborns and infants, then calculating and analyzing the features of the baby's face related to swallowing with artificial intelligence. investigators would like to analyze the relationship between this evaluation result and future development.