2021 Fiscal Year Final Research Report
Pathology diagnosis AI for oral premalignancies
Project/Area Number |
19K10084
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 57020:Oral pathobiological science-related
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Research Institution | Tokyo Medical and Dental University |
Principal Investigator |
Sakamoto Kei 東京医科歯科大学, 大学院医歯学総合研究科, 講師 (00302886)
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Project Period (FY) |
2019-04-01 – 2022-03-31
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Keywords | 口腔がん / AI |
Outline of Final Research Achievements |
Most oral cancers are developed from precancerous lesions. The definitive diagnosis of precancerous lesions is made by pathological examination, and the risk of cancer transformation is assessed by the grade of atypia. However, since there are no quantitative criteria for grading, the judgment can be varied across observers. In this study, we analyzed histopathological images of oral cancer and precancerous lesions using AI to investigate the possibility of applying AI to the diagnosis of oral pathology. 90% of the diagnoses made by evaluating the heat map created based on AI predictions were in agreement with those judged from the actual histological images. The interpretation of the heat map did not require knowledge of histopathology, and was considered to be effective in standardizing the pathological examination of precancerous lesions in the oral cavity.
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Free Research Field |
病理学
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Academic Significance and Societal Importance of the Research Achievements |
口腔がん・前がん病変は顕微鏡観察により、組織学的形態の変化を評価して診断される。しかし、その判定はかなり主観的要素が占める割合が多く、判定者間の不一致もみられる。いくつかの評価項目についての評点を加算してスコア化する、判定量的な判定法も提唱されているが、やはり再現性は十分でなかった。今回の手法ではAIを用いた画像解析により組織の異型性をヒートマップとしてほぼ正確に変換することができた。ヒートマップから診断や治療方針を導きだすことは、組織学的な知識がなくても容易であった。数値として出力される臨床検査結果と同様の簡便さを備えて利用できる有用な技法と考えられた。
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