Kuramoto Naomi, Ichimura Kazuhiro, Jayatilake Dushyantha, Shimokakimoto Tomoya, Hidaka Kikue, Suzuki Kenji
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4365-4368. doi: 10.1109/EMBC44109.2020.9176721.
Aspiration pneumonia is a life-threatening disease for the elderly. To prevent its risk, regular swallowing assessment is necessary; however, current screening tools for swallow assessment are not widely available and medical experts are insufficient. As a portable assessment tool, we have been developing a smartphone-based realtime monitoring device (GOKURI) which can evaluate swallowing ability based on swallow sounds. For better detection accuracy of the system, we integrated a deep learning model which was developed based on the swallowing anatomy. In this paper, we provide a detailed analysis to see how the swallow sounds detected by the deep learning-based monitor correspond to the actual swallow activities. Also, as an example of practical application of the system, we analyzed the changes of the swallow abilities over time by recording swallow sounds twice for the same participants at a nursing home. To minimize the risk of aspiration pneumonia, caregivers need to understand the disability levels of the patient's swallows so that safe feeding assistance can be provided. The result of this paper implies the possibility of using GOKURI as a daily swallowing monitor with minimum interventions.
吸入性肺炎对老年人来说是一种危及生命的疾病。为预防其风险，定期进行吞咽评估是必要的；然而，目前用于吞咽评估的筛查工具并不普及，且医学专家也不足。作为一种便携式评估工具，我们一直在开发一种基于智能手机的实时监测设备（GOKURI），它可以根据吞咽声音评估吞咽能力。为提高系统的检测准确性，我们集成了一个基于吞咽解剖结构开发的深度学习模型。在本文中，我们进行了详细分析，以了解基于深度学习的监测器检测到的吞咽声音与实际吞咽活动之间的对应关系。此外，作为该系统实际应用的一个例子，我们通过在养老院对同一参与者进行两次吞咽声音记录，分析了吞咽能力随时间的变化。为将吸入性肺炎的风险降至最低，护理人员需要了解患者吞咽的残疾程度，以便提供安全的喂食协助。本文的结果表明了将GOKURI用作日常吞咽监测工具且干预最少的可能性。
