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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
重度の閉塞型睡眠時無呼吸症候群の鑑別を目的とした上気道MRIの画像解析とその有用性
| Title: | 重度の閉塞型睡眠時無呼吸症候群の鑑別を目的とした上気道MRIの画像解析とその有用性 |
| Other Titles: | Usefulness of Image Analysis on Upper Airway MRI for the Classification of Severe Obstructive Sleep Apnea Syndrome (OSAS) |
| Authors: | 三上, 剛1 Browse this author →KAKEN DB | | 米澤, 一也2 Browse this author →KAKEN DB | | 小島, 洋一郎3 Browse this author →KAKEN DB | | 山本, 雅人4 Browse this author →KAKEN DB | | 古川, 正志5 Browse this author →KAKEN DB |
| Authors(alt): | MIKAMI, Tsuyoshi1 | | YONEZAWA, Kazuya2 | | KOJIMA, Yohichiro3 | | YAMAMOTO, Masahito4 | | FURUKAWA, Masashi5 |
| Keywords: | Sleep Apnea Syndrome | | Upper Airway MRI | | Apnea/Hypopnea Index |
| Issue Date: | 2014 |
| Publisher: | MEDICAL IMAGING AND INFORMATION SCIENCES |
| Journal Title: | 医用画像情報学会雑誌 |
| Volume: | 31 |
| Issue: | 1 |
| Start Page: | 13 |
| End Page: | 18 |
| Publisher DOI: | 10.11318/mii.31.13 |
| Abstract: | This paper proposes an evaluation index for the classification of severe Obstructive Sleep Apnea Syndrome (OSAS) by the use of the tongue morphology and the cross sectional area of the narrowest upper airway evaluated by image analysis on the upper airway MRI. In general, polysomnography (PSG) is a gold standard evaluation for severe SAS, but sometimes the symptoms do not occur during PSG screening because of the first night effect. The morphology of the upper airway, on the other hand, gives much information about the severity of OSAS. So, the upper airway MRI is often used for medical diagnosis, but few evaluation indices have been reported objectively. We focused on the tongue region and considered two directions from the center to the edge of the tongue higher correlated with the severity and determined the cross sectional area in the narrowest upper airway. These features are weighted and linearly combined to predict the severity. Finally, the severe patients are detected by judging whether the prediction value is greater than or equal to 30. As a result, the true positive rate is 0.909 and the false positive rate is 0.476 for detecting the severe patients. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/64509 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 山本 雅人
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