Updated on 2022/04/26

写真a

 
FURUKAWA Taiki
 
Organization
Nagoya University Hospital Medical IT Center Designated assistant professor
Title
Designated assistant professor

Degree 1

  1. 博士(医学) ( 2021.3   名古屋大学 ) 

Research Areas 2

  1. Life Science / Respiratory medicine

  2. Informatics / Life, health and medical informatics  / 医療情報学

Research History 2

  1. Nagoya University   Medical IT Center, Hospital   Designated assistant professor

    2021.4

  2. RIKEN

    2021.4

Professional Memberships 4

  1. Japan Association for Medical Informatics

    2021.5

  2. The Japanese Society for Artificial Intelligence

    2021.4

  3. Japanese Respiratory Society

    2013.10

  4. Japanese Society of Allergology

 

Papers 7

  1. Prevalence and prognosis of chronic fibrosing interstitial lung diseases with a progressive phenotype. Reviewed International journal

    Reoto Takei, Kevin K Brown, Yasuhiko Yamano, Kensuke Kataoka, Toshiki Yokoyama, Toshiaki Matsuda, Tomoki Kimura, Atsushi Suzuki, Taiki Furukawa, Junya Fukuoka, Takeshi Johkoh, Yoshihito Goto, Yasuhiro Kondoh

    Respirology (Carlton, Vic.)   Vol. 27 ( 5 ) page: 333 - 340   2022.3

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    Language:English   Publishing type:Research paper (scientific journal)  

    BACKGROUND AND OBJECTIVE: The development of clinically progressive fibrosis complicates a wide array of interstitial lung diseases (ILDs). However, there are limited data regarding its prevalence and prognosis. METHODS: We analysed consecutive patients seen for initial evaluation of a fibrosing form of ILD (FILD). Patients were evaluated for evidence of progressive fibrosis over the first 24 months of follow-up. We defined a progressive phenotype as the presence of at least one of the following: a relative decline in forced vital capacity (FVC) of ≥10%; a relative decline in FVC of ≥5%-<10% with a relative decline in diffusing capacity of the lung for carbon monoxide of ≥15%, increased fibrosis on HRCT or progressive symptoms. RESULTS: Eight hundred and forty-four patients (397 with idiopathic pulmonary fibrosis [IPF] and 447 non-IPF FILD) made up the final analysis cohort. Three hundred and fifty-five patients (42.1%) met the progressive phenotype criteria (59.4% of IPF patients and 26.6% of non-IPF FILD patients, p <0.01). In both IPF and non-IPF FILD, transplantation-free survival differed between patients with a progressive phenotype and those without (p <0.01). Multivariable analysis showed that a progressive phenotype was an independent predictor of transplantation-free survival (hazard ratio [HR]: 3.36, 95% CI: 2.68-4.23, p <0.01). Transplantation-free survival did not differ between non-IPF FILD with a progressive phenotype and IPF (HR: 1.12, 95% CI: 0.85-1.48, p = 0.42). CONCLUSION: Over one-fourth of non-IPF FILD patients develop a progressive phenotype compared to approximately 60% of IPF patients. The survival of non-IPF FILD patients with a progressive phenotype is similar to IPF.

    DOI: 10.1111/resp.14245

    Web of Science

    Scopus

    PubMed

  2. The prognostic value of the COPD Assessment Test in fibrotic interstitial lung disease. Reviewed International journal

    Toshiaki Matsuda, Yasuhiro Kondoh, Taiki Furukawa, Atsushi Suzuki, Reoto Takei, Hajime Sasano, Yasuhiko Yamano, Toshiki Yokoyama, Kensuke Kataoka, Tomoki Kimura

    Respiratory investigation   Vol. 60 ( 1 ) page: 99 - 107   2022.1

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

    BACKGROUND: The COPD Assessment Test (CAT) has been studied as a measure of health status in idiopathic pulmonary fibrosis (IPF) and interstitial lung disease associated with connective tissue disease. However, its prognostic value is unknown. The present study explored the association between CAT score and mortality in fibrotic interstitial lung disease (FILD), including IPF and other forms of ILD. METHODS: We retrospectively analyzed 501 consecutive patients with FILD who underwent clinical assessment, including pulmonary function test and CAT. The association between CAT score and 3-year mortality was assessed using Cox proportional hazard analysis, Kaplan-Meier plots, and the log-rank test for trend. To handle missing data, the imputed method was used. RESULTS: The patients' median age was 68 years, and 320 were male (63.9%). Regarding CAT severity, 203 patients had a low impact level (score <10), 195 had a medium level (10-20), 80 had a high level (21-30), and 23 had a very high level (31-40). During the 3-year study period, 118 patients died. After adjusting for age, sex, forced vital capacity, diffusion capacity for carbon monoxide, IPF diagnosis, and usual interstitial pneumonia pattern on high-resolution computed tomography, the CAT score was significantly associated with 3-year mortality (hazard ratio in increments of 10 points: 1.458, 95% confidence interval 1.161-1.830; p < 0.001). In addition, patients with high and very high impact levels had twofold and threefold higher mortality risk than those with low levels, respectively. CONCLUSION: The CAT has prognostic value in FILD.

    DOI: 10.1016/j.resinv.2021.07.007

    Web of Science

    Scopus

    PubMed

  3. The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence. Reviewed International journal

    Ichidai Tanaka, Taiki Furukawa, Masahiro Morise

    Cancer cell international   Vol. 21 ( 1 ) page: 454 - 454   2021.8

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    Language:English   Publishing type:Research paper (scientific journal)  

    Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.

    DOI: 10.1186/s12935-021-02165-7

    Web of Science

    Scopus

    PubMed

  4. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF Reviewed International journal

    Ito T., Takei R., Sasano H., Yamano Y., Yokoyama T., Matsuda T., Kimura T., Furukawa T., Johkoh T., Fukuoka J., Kondoh Y.

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE   Vol. 203 ( 9 )   2021.5

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    Language:Japanese   Publishing type:Research paper (international conference proceedings)  

    Web of Science

  5. Impact of post-capillary pulmonary hypertension on mortality in interstitial lung disease. Reviewed International journal

    Ryo Teramachi, Hiroyuki Taniguchi, Yasuhiro Kondoh, Tomoki Kimura, Kensuke Kataoka, Toshiki Yokoyama, Taiki Furukawa, Mitsuaki Yagi, Koji Sakamoto, Naozumi Hashimoto, Yoshinori Hasegawa

    Respiratory investigation   Vol. 59 ( 3 ) page: 342 - 349   2021.5

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    Language:English   Publishing type:Research paper (scientific journal)  

    BACKGROUND: Pulmonary hypertension (PH) influences mortality in patients with interstitial lung disease (ILD). Almost all studies on patients with ILD, have focused on the clinical impact of pre-capillary PH on survival. Therefore, little is known about the influence of post-capillary PH. We aimed to assess the prevalence of post-capillary PH and its clinical impact on survival in patients with ILD, followed by comparison with pre-capillary PH. METHODS: This retrospective study enrolled 1152 patients with ILD who were diagnosed with PH using right heart catheterization between May 2007 and December 2015. We analyzed the demographics and composite outcomes (defined as death from any cause or lung transplantation) of patients with post-capillary PH and compared them with patients with pre-capillary PH. RESULTS: Thirty-two (20%) of the 157 patients with ILD-PH were diagnosed with post-capillary PH. Patients with post-capillary PH had significantly lower modified Medical Research Council scores, higher diffusion capacity for carbon monoxide, higher resting PaO2, lower pulmonary vascular resistance (PVR), and higher lowest oxygen saturation during the 6-min walk test compared to those with pre-capillary PH. Cardiovascular diseases were associated with a higher risk of mortality in patients with post-capillary PH. Multivariate Cox proportional hazards analysis demonstrated no significant difference between the composite outcomes in pre-capillary and post-capillary PH, while PVR and the ILD Gender-Age-Physiology Index were significantly associated with the composite outcome. CONCLUSIONS: We found that approximately one-fifth of patients with ILD-PH were diagnosed with post-capillary PH, and that PVR and not post-capillary PH was associated with mortality.

    DOI: 10.1016/j.resinv.2020.12.010

    Web of Science

    Scopus

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  6. Smart hospital infrastructure: geomagnetic in-hospital medical worker tracking. Reviewed International journal

    Keiko Yamashita, Shintaro Oyama, Tomohiro Otani, Satoshi Yamashita, Taiki Furukawa, Daisuke Kobayashi, Kikue Sato, Aki Sugano, Chiaki Funada, Kensaku Mori, Naoki Ishiguro, Yoshimune Shiratori

    Journal of the American Medical Informatics Association : JAMIA   Vol. 28 ( 3 ) page: 477 - 486   2021.3

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    Language:English   Publishing type:Research paper (scientific journal)  

    PURPOSE: Location visualization is essential for locating people/objects, improving efficiency, and preventing accidents. In hospitals, Wi-Fi, Bluetooth low energy (BLE) Beacon, indoor messaging system, and similar methods have generally been used for tracking, with Wi-Fi and BLE being the most common. Recently, nurses are increasingly using mobile devices, such as smartphones and tablets, while shifting. The accuracy when using Wi-Fi or BLE may be affected by interference or multipath propagation. In this research, we evaluated the positioning accuracy of geomagnetic indoor positioning in hospitals. MATERIALS AND METHODS: We compared the position measurement accuracy of a geomagnetic method alone, Wi-Fi alone, BLE beacons alone, geomagnetic plus Wi-Fi, and geomagnetic plus BLE in a general inpatient ward, using a geomagnetic positioning algorithm by GiPStech. The existing Wi-Fi infrastructure was used, and 20 additional BLE beacons were installed. Our first experiment compared these methods' accuracy for 8 test routes, while the second experiment verified a combined geomagnetic/BLE beacon method using 3 routes based on actual daily activities. RESULTS: The experimental results demonstrated that the most accurate method was geomagnetic/BLE, followed by geomagnetic/Wi-Fi, and then geomagnetic alone. DISCUSSION: The geomagnetic method's positioning accuracy varied widely, but combining it with BLE beacons reduced the average position error to approximately 1.2 m, and the positioning accuracy could be improved further. We believe this could effectively target humans (patients) where errors of up to 3 m can generally be tolerated. CONCLUSION: In conjunction with BLE beacons, geomagnetic positioning could be sufficiently effective for many in-hospital localization tasks.

    DOI: 10.1093/jamia/ocaa204

    Web of Science

    Scopus

    PubMed

  7. Serum mitochondrial DNA predicts the risk of acute exacerbation and progression of idiopathic pulmonary fibrosis. Reviewed International coauthorship International journal

    Koji Sakamoto, Taiki Furukawa, Yasuhiko Yamano, Kensuke Kataoka, Ryo Teramachi, Anjali Walia, Atsushi Suzuki, Masahide Inoue, Yoshio Nakahara, Changwan Ryu, Naozumi Hashimoto, Yasuhiro Kondoh

    The European respiratory journal   Vol. 57 ( 1 )   2021.1

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1183/13993003.01346-2020

    Web of Science

    Scopus

    PubMed

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Books 2

  1. 間質性肺炎診療と新テクノロジー

    古川大記( Role: Sole author)

    呼吸器内科学レビュー 2022-’23  2021.12 

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    Language:Japanese Book type:Scholarly book

  2. 間質性肺炎のAI診断

    古川大記, 大山慎太郎( Role: Joint author)

    呼吸器ジャーナル  2021.8 

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    Language:Japanese Book type:Scholarly book

MISC 7

  1. 肺線維症の病態に関連する2つの新規マーカー分子:メフリンとミトコンドリアDNA

    阪本 考司, 橋本 直純, 中原 義夫, 古川 大記

    呼吸器内科   Vol. 41 ( 2 ) page: 197 - 201   2022.2

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    Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)   Publisher:(有)科学評論社  

  2. 【びまん性肺疾患における多職種合議(MDD)診断とAI支援の現在と未来】MDD診断へのAI「画像診断」支援の現状と可能性について

    古川 大記, 寺町 涼

    呼吸器内科   Vol. 41 ( 2 ) page: 180 - 184   2022.2

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)   Publisher:(有)科学評論社  

  3. 【最新主要文献とガイドラインでみる呼吸器内科学レビュー 2022-'23】(XIV章)新テクノロジーと肺疾患 間質性肺炎診療と新テクノロジー

    古川 大記

    呼吸器内科学レビュー   Vol. 2022-'23   page: 327 - 332   2021.12

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)   Publisher:(株)総合医学社  

    間質性肺炎(ILD)の正確な診断には、呼吸器内科医、放射線科医、病理医の集学的議論(MDD)、外科的肺生検やクライオバイオプシーが重要であるが、近年、新しいテクノロジーを用いた診療が広がりつつある。機械学習と深層学習を用いたILDの画像所見の抽出、ゲノム評価、診断と治療効果予測について概説した。また、間質性肺炎のAI(人工知能)創薬、遠隔診療(オンラインMDD診断)、在宅モニタリングと呼吸器リハビリテーションについて述べた。

  4. 特集 間質性肺炎 徹底討論!-鳥からは逃げられない過敏性肺炎,放置してよいのかILA Ⅳ.最近の話題 間質性肺炎のAI診断

    古川 大記, 大山 慎太郎

    呼吸器ジャーナル   Vol. 69 ( 3 ) page: 450 - 457   2021.8

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    Publisher:株式会社 医学書院  

    DOI: 10.11477/mf.1437200484

    CiNii Research

  5. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF Reviewed International journal

    Ito T., Takei R., Sasano H., Yamano Y., Yokoyama T., Matsuda T., Kimura T., Furukawa T., Johkoh T., Fukuoka J., Kondoh Y.

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE   Vol. 203 ( 9 )   2021.5

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    Language:English   Publishing type:Research paper, summary (international conference)  

    Web of Science

  6. 間質性肺疾患における肺高血圧予測モデルの構築 Reviewed

    佐藤 智則, 古川 大記, 寺町 涼, 山野 泰彦, 横山 俊樹, 松田 俊明, 片岡 健介, 木村 智樹, 近藤 康博

    日本呼吸器学会誌   Vol. 10 ( 増刊 ) page: 225 - 225   2021.4

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    Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)   Publisher:(一社)日本呼吸器学会  

  7. Construction of a data collection platform based on the regional medical care data infrastructure Reviewed

    佐藤菊枝, 小林大介, 小林大介, 山下暁士, 大山慎太郎, 古川大記, 白鳥義宗

    日本医療情報学会春季学術大会プログラム・抄録集   Vol. 25th   2021

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Presentations 2

  1. 統合診療データを用いたAIによるアウトカム予測と 診療サマリ生成に向けた検討

    古川 大記, 三沢 翔太郎, 大山 慎太郎, 佐藤 菊枝, 狩野 竜示, 鑓水 大和, 白鳥 義宗

    第41回医療情報学連合大会  2021.11.20 

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    Event date: 2021.11

    Presentation type:Oral presentation (general)  

  2. Interstitial lung disease and BIG-DATA / AI Invited

    Taiki Furukawa

    The 61st Annual Meeting of The Japanese Respiratory Society  2021.4.24 

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    Event date: 2021.4

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

Research Project for Joint Research, Competitive Funding, etc. 6

  1. All Japan大規模レジストリデータを背景とした間質性肺炎の治療プログラム及びデバイスの開発

    2022.4 - 2026.3

    医療機器等研究成果展開事業 

    中澤 公貴, 五十嵐亮レオナルド, 大山慎太郎

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    Authorship:Principal investigator  Grant type:Competitive

  2. 間質性肺炎に対する多施設共同前向き観察研究

    2020.3 - 2025.12

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    Authorship:Coinvestigator(s)  Grant type:Collaborative (industry/university)

  3. 医師の業務効率化を支援するアルゴリズムの機械学習

    2021.11 - 2023.10

    白鳥義宗, 佐藤菊枝, 大山慎太郎, 古川大記

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    Authorship:Coinvestigator(s)  Grant type:Collaborative (industry/university)

  4. 特発性間質性肺炎の前向きレジストリの構築とインタラクティブMDD診断システムを用いた診断標準化に基づく疫学データの創出―人工知能(AI)診断システムと新規バイオマーカーの開発―

    2020.4 - 2022.3

    難治性疾患等実用化研究事業 

    須田 隆文, 井上 義一, 横田 秀夫, 宮崎 泰成, 近藤 康博, 古川 大記, 坂東 政司, 小倉 高志, 上甲 剛, 長谷川 好規, 白鳥 義宗, 福岡 順也, 本間 栄

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    Authorship:Coinvestigator(s)  Grant type:Competitive

  5. びまん性肺疾患MDD診断のための双方向性Webプラットフォーム構築と人工知能診断の社会実装に関する前向き研究

    2019.4 - 2022.3

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    Authorship:Principal investigator  Grant type:Competitive

  6. びまん性肺疾患診断の臨床画像クラウド型統合データベースの基盤構築と機械学習による診断・予後予測アルゴリズム構築に関する研究

    2019.4 - 2022.3

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    Authorship:Principal investigator  Grant type:Competitive

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KAKENHI (Grants-in-Aid for Scientific Research) 4

  1. Immune-pathological diagnostic artificial intelligence development research for pulmonary fibrosis using fibrotic foci-specific enhanced micro-CT

    Grant number:20K21599  2020.7 - 2023.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Research (Exploratory)  Grant-in-Aid for Challenging Research (Exploratory)

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    Authorship:Coinvestigator(s)  Grant type:Competitive

  2. 特発性間質性肺炎の前向きレジストリの構築とインタラクティブMDD診断システムを用いた診断標準化に基づく疫学データの創出―人工知能(AI)診断システムと新規バイオマーカーの開発―

    2020.4 - 2022.3

    日本医療研究開発機構(AMED)  難治性疾患等実用化研究事業 

    須田 隆文, 井上 義一, 横田 秀夫, 宮崎 泰成, 近藤 康博, 古川 大記, 坂東 政司, 小倉 高志, 上甲 剛, 長谷川 好規, 白鳥 義宗, 福岡 順也, 本間 栄

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    Authorship:Coinvestigator(s) 

  3. Orality and Narrative Technique in Pain Clinic

    Grant number:19KT0027  2019.7 - 2022.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)  Grant-in-Aid for Scientific Research (C)

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    Authorship:Coinvestigator(s)  Grant type:Competitive

  4. びまん性肺疾患の診断と予後予測における機械学習アルゴリズム構築に関する研究

    Grant number:19K17633  2019.4 - 2022.3

    日本学術振興会  科学研究費助成事業 若手研究  若手研究

    古川 大記

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    Authorship:Principal investigator  Grant type:Competitive

    Grant amount:\4290000 ( Direct Cost: \3300000 、 Indirect Cost:\990000 )

    びまん性肺疾患は一般の呼吸器科医には診断が困難な上に予後不良な群が含まれる一方、精度の高い診断を行える専門医が少ないため、人工知能(AI)による精度の高い診断システムと予後予測システムの開発、及び開発に必要な大規模なデータベースが望まれてきた。このため、びまん性肺疾患の臨床情報・画像データのデータベース構築と、精度の高いびまん性肺疾患診断・予後予測AI開発を行い、一般に利用できる形を検討する。
    びまん性肺疾患は一般の呼吸器科医には診断が困難なことが多い上に予後不良な群が含まれる一方,精度の高い診断を行える専門医が少ないため,人工知能(AI)による精度の高い診断システムと予後予測システムの開発,及び開発に必要な大規模なデータベースが望まれてきた.本研究の全体計画における目標は「びまん性肺疾患の臨床情報・画像データのデータベース構築と,精度の高いびまん性肺疾患診断・予後予測人工知能(AI)開発」である.具体的には(A)医療用データベースの構築,(B)臨床情報・画像データと医療知識のマッピング,(C)ディープラーニングを含めた機械学習による医療画像・臨床情報からの自動所見抽出,(D)びまん性肺疾患診断・予後予測人工知能(AI)を開発することである.
    本研究では令和1年度に,全国のびまん性肺疾患専門病院からびまん性肺疾患の系統だった疾患データ 約2,000例を蓄積してデータベースを構築し,医療知識の適切なマッピング・種々の前処理を含め,AI診断に適したデータ変換を行った.さらに,びまん性肺疾患の診断予測を行うAIを構築したところ,単施設データで作成された診断AIモデルと同等の精度を達成した.
    当該年度(令和2年度)では,令和1年度に構築した診断AIアルゴリズムのブラッシュアップを行った.さらに,予後情報に対して,臨床情報と昨年度に構築した診断AIの特徴量を用いてディープラーニングと機械学習を組み合わせ,精度の高い予後予測AIを構築した.構築した予後予測AIを応用し,個々人での治療効果予測を可能とするアルゴリズム構築を達成した.このアルゴリズムにより,臨床情報からの個別化医療への道筋を示した.
    本研究は,当該年度(令和2年度)に間質性肺炎の予後情報に対して,胸部CT画像・臨床情報を用いてディープラーニングと機械学習を組み合わせ,精度の高い予後予測AIの構築を目的とし,目的達成のために以下の課題を遂行することを掲げてきた.
    1. 精度の高い予後予測AIを構築.
    <BR>
    令和2年度には,開発済みのびまん性肺疾患AI診断システムをブラッシュアップし,より高精度で汎化性能の高いAI診断システムを構築した.また,構築したシステムから胸部CT画像の特徴量抽出に成功した.さらに,この特徴量とその他の臨床情報を用いて,予後情報に対する深層学習を行い,高精度の予後予測AIを構築した.これらの結果は令和2年度に国内外で発表を行っており,当初の計画通り概ね順調に進展していると考える.
    本研究の解析によって,多施設データに対する適切な前処理方法,解析の高速化方法がわかっている.また,高精度予後予測AIを応用した個々人への治療効果予測アルゴリズムを構築した.
    令和3年度には,令和2年度で構築した予後予測AIは個別化医療への道を開くものであるため,本領域に与える影響が大きく,より多くの施設で精度が落ちないように汎化性能を高めるためのアルゴリズムブラッシュアップを行っていく.令和2年度に想定していた研究参加施設からのデータ提供が遅れたため,データ解析量が当初の想定より減少していたが,データ収集が完了したため,令和3年度に専用ワークステーションを購入し,さらなるバリデーションを行う予定である.
    研究発表は,コロナ禍でオンラインによる発表に変更になったため旅費使用額が減少したが,令和3年度には,これまでの研究成果を国内外で積極的に発表するとともに,全ての結果をまとめたジャーナル投稿を予定している.
    また,作成したびまん性肺疾患診断AIと予後予測AIをウェブ上に組み込んで,広く一般に利用できるプラットフォームを構築する.
    現在,大規模前向き全国レジストリを開始しており,この中で前向きに人の診断とAIの診断を施設に提示するプラットフォーム構築に取り掛かっており,臨床現場における洗練化と精度向上,新規知見の創出を行いたい.

Industrial property rights 1

  1. 情報処理装置、情報処理方法、および、コンピュータプログラム

    神山 潤二, 古川 大記, 森瀬 昌宏, 横田 秀夫

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    Application no:特願2022-043291  Date applied:2022.3

 

Teaching Experience (Off-campus) 1

  1. 医療情報学

    2021.4