@article{oai:ipsj.ixsq.nii.ac.jp:00234958,
 author = {耿, 世嫻 and 平井, 雄太 and 下島, 銀士 and 柳田, 陵介 and 山田, 大志 and 小野寺, 宏 and 戸原, 玄 and 矢谷, 浩司 and Shixian, Geng and Yuta, Hirai and Ginshi, Shimojima and Ryosuke, Yanagida and Taishi, Yamada and Hiroshi, Onodera and Haruka, Tohara and Koji, Yatani},
 issue = {6},
 journal = {情報処理学会論文誌},
 month = {Jun},
 note = {医師による嚥下障害の診断には，患者の定期的な通院が必要であり，診断結果は医師の経験に大きく影響されるという課題がある．そこで我々は，患者が在宅で簡単に嚥下障害の可能性の有無を評価できるように，スマートフォンで撮影した動画から嚥下機能を評価する手法を提案する．この提案手法の実現に向けて，必要なタスク群を決定し，147名の実験参加者から得られた動画を分析した．その結果，70.6%の精度（Balanced Accuracy）とWeighted F1 scoreは0.801でランダムの予測スコアより0.243ポイント高い識別性能を得ることができた．, The diagnosis of dysphagia by clinicians poses challenges, as it requires patients to make regular clinic visits and the diagnosis requires training and empirical skills of the doctors. To address these issues and enable patients to assess potential symptoms of dysphagia easily at home, we propose a method to evaluate oral and swallowing function from videos captured by smartphones. To achieve this, we determined the required set of tasks for analyzing oral and swallowing function and analyzed videos of these tasks obtained from 147 participants. As a result, we obtained a discrimination performance with a balanced accuracy of 70.6% and a Weighted F1 score of 0.801. The Weighted F1 score of our model (0.801) is 0.243 points better than that of a random predictor.},
 pages = {1091--1101},
 title = {スマートフォンを用いた画像認識による嚥下機能の定量的評価手法},
 volume = {65},
 year = {2024}
}