| Project/Area Number |
18K09315
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 56050:Otorhinolaryngology-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Kashio Akinori 東京大学, 医学部附属病院, 准教授 (20451809)
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| Co-Investigator(Kenkyū-buntansha) |
赤松 裕介 東京大学, 医学部附属病院, 言語聴覚士 (00794869)
尾形 エリカ 東京大学, 医学部附属病院, 言語聴覚士 (20794853)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | ECAP / 人工内耳 / 予後予測 / 機械学習 / AI / 予後 / 成績 / NRT / 内耳奇形 / 髄膜炎 / ＥＣＡＰ / 聴取能 |
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| Outline of Final Research Achievements |
The ECAP of cochlear implant patients was measured; the ECAP response was examined by pathological condition, and it was inferred that in cases of meningitis and cochlear canal stenosis, the postoperative hearing prognosis was also poor if no response was observed, thus helping to predict the prognosis. When the left and right ECAPs of patients with hearing loss due to GJB2 gene abnormality with the same electrode inserted on both sides were examined, it was found that in many cases the growth function of the left and right ECAPs differed. These results suggest that in GJB2 hearing loss, the intracochlear conditions in the left and right cochlea differ considerably. A machine learning model was developed to predict post-operative cochlear thresholds using the accumulated ECAP data, which was more accurate than ECAP alone when machine learning took into account data such as electrode type, age and medical condition.
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| Academic Significance and Societal Importance of the Research Achievements |
人工内耳患者のECAP測定を通じて人工内耳の予後及び、蝸牛内の病態が推測できることを示した。本結果は人工内耳術後の予後を予測し、その後の療育・リハビリテーションの立案などに重要な情報を提供できるものであると考えられる。また、ECAPのデータおよびその他の情報を機械学習で評価することにより術後閾値レベルの予測が、可能であることを示した。本成果は小児など閾値評価が困難な症例に対してより早期に適切な設定をもたらすことが可能となり人工内耳医療の発展につながると考えられた。
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