炎症性腸疾患の診断と治療におけるAIの現状
人工知能(AI)を用いた技術が,炎症性腸疾患(IBD)の診療において重要な役割を果たすことが期待されている.AIは特に内視鏡分野に応用されており,IBDの病態把握を向上させる報告がされている.これらのAIによる内視鏡診断技術は,診断の精度向上,内視鏡画像解釈のばらつきの軽減,そして臨床医の意思決定プロセスの支援を目的としている.AIを活用することで,医療提供者はより個別化された効果的な治療を提供する可能性が高まり,最終的にはIBD診療における患者の治療結果を改善させることが期待されている....
Saved in:
Published in | 昭和医科大学雑誌 Vol. 85; no. 3; pp. 193 - 198 |
---|---|
Main Authors | , , , , , , , |
Format | Journal Article |
Language | Japanese |
Published |
昭和医科大学学士会
2025
|
Subjects | |
Online Access | Get full text |
ISSN | 2759-8144 2759-8152 |
DOI | 10.14930/jsmu.85.3_193 |
Cover
Abstract | 人工知能(AI)を用いた技術が,炎症性腸疾患(IBD)の診療において重要な役割を果たすことが期待されている.AIは特に内視鏡分野に応用されており,IBDの病態把握を向上させる報告がされている.これらのAIによる内視鏡診断技術は,診断の精度向上,内視鏡画像解釈のばらつきの軽減,そして臨床医の意思決定プロセスの支援を目的としている.AIを活用することで,医療提供者はより個別化された効果的な治療を提供する可能性が高まり,最終的にはIBD診療における患者の治療結果を改善させることが期待されている. |
---|---|
AbstractList | 人工知能(AI)を用いた技術が,炎症性腸疾患(IBD)の診療において重要な役割を果たすことが期待されている.AIは特に内視鏡分野に応用されており,IBDの病態把握を向上させる報告がされている.これらのAIによる内視鏡診断技術は,診断の精度向上,内視鏡画像解釈のばらつきの軽減,そして臨床医の意思決定プロセスの支援を目的としている.AIを活用することで,医療提供者はより個別化された効果的な治療を提供する可能性が高まり,最終的にはIBD診療における患者の治療結果を改善させることが期待されている. |
Author | 工藤, 進英 一政, 克朗 小形, 典之 馬場, 俊之 若村, 邦彦 三澤, 将史 林, 武雅 前田, 康晴 |
Author_xml | – sequence: 1 fullname: 小形, 典之 organization: 昭和医科大学横浜市北部病院消化器センター – sequence: 1 fullname: 林, 武雅 organization: 昭和医科大学横浜市北部病院消化器センター – sequence: 1 fullname: 若村, 邦彦 organization: 昭和医科大学横浜市北部病院消化器センター – sequence: 1 fullname: 三澤, 将史 organization: 昭和医科大学横浜市北部病院消化器センター – sequence: 1 fullname: 工藤, 進英 organization: 昭和医科大学横浜市北部病院消化器センター – sequence: 1 fullname: 一政, 克朗 organization: 昭和医科大学横浜市北部病院消化器センター – sequence: 1 fullname: 前田, 康晴 organization: 昭和医科大学横浜市北部病院消化器センター – sequence: 1 fullname: 馬場, 俊之 organization: 昭和医科大学横浜市北部病院消化器センター |
BookMark | eNo9j81Kw0AcxBepYK29-haJ-2k2CEIpfhQKXvS8_JNsNKGtktSDNxMQoQXFS8WzBwuiBw8iCn2ZQLaPYYpSGH4zMDAw66g2OB9ohDYJtgl3Gd6K0_6lLYXNFHHZCqpTR7iWJILWlpnzNdRM08jDQjqcuy6uox2T35nH2_L6ZX7zZSazMn8usvf59LucvBXZtPz4MU95kb0W2ajIHop83OpUvbmfmdHnBloNoZfq5r830Mn-3nH70OoeHXTara4VE0eCRQmTWm9XwlgI0IIwzgMqiKMDwNQB7njc1dyXjID0wfdCGgQVIQw09YA10O7fbpwO4VSriyTqQ3KlIBlGfk-rxXUlhWILVO-XhX8GiYqB_QKs_Wrp |
ContentType | Journal Article |
Copyright | 2025 昭和医科大学学士会 |
Copyright_xml | – notice: 2025 昭和医科大学学士会 |
DOI | 10.14930/jsmu.85.3_193 |
DatabaseTitleList | |
DeliveryMethod | fulltext_linktorsrc |
EISSN | 2759-8152 |
EndPage | 198 |
ExternalDocumentID | article_jsmu_85_3_85_193_article_char_ja |
GroupedDBID | M~E |
ID | FETCH-LOGICAL-j178a-2138ee6ee60055ae51344d2517eda027a47b49e4c831a8cacbf2ddcbfafde2ba3 |
ISSN | 2759-8144 |
IngestDate | Wed Sep 03 06:30:55 EDT 2025 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Issue | 3 |
Language | Japanese |
LinkModel | OpenURL |
MergedId | FETCHMERGED-LOGICAL-j178a-2138ee6ee60055ae51344d2517eda027a47b49e4c831a8cacbf2ddcbfafde2ba3 |
OpenAccessLink | https://www.jstage.jst.go.jp/article/jsmu/85/3/85_193/_article/-char/ja |
PageCount | 6 |
ParticipantIDs | jstage_primary_article_jsmu_85_3_85_193_article_char_ja |
PublicationCentury | 2000 |
PublicationDate | 2025 |
PublicationDateYYYYMMDD | 2025-01-01 |
PublicationDate_xml | – year: 2025 text: 2025 |
PublicationDecade | 2020 |
PublicationTitle | 昭和医科大学雑誌 |
PublicationTitleAlternate | 昭和医大誌 |
PublicationYear | 2025 |
Publisher | 昭和医科大学学士会 |
Publisher_xml | – name: 昭和医科大学学士会 |
References | 3) Schroeder KW, Tremaine WJ, Ilstrup DM. Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. a randomized study. N Engl J Med. 1987;317:1625-1629. 5) Travis SP, Schnell D, Krzeski P, et al. Reliability and initial validation of the ulcerative colitis endoscopic index of severity. Gastroenterology. 2013;145:987-995. 8) Kuroki T, Maeda Y, Kudo SE, et al. A novel artificial intelligence-assisted “vascular-healing” diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video). Gastrointest Endosc. 2024;100:97-108. 9) Takenaka K. Ohtsuka K, Fujii T, et al. Development and validation of a deep neural network for accurate evaluation of endoscopic images from patients with ulcerative colitis. Gastroenterology. 2020;158:2150-2157. 15) Tsai L, Ma C, Dulai PS, et al. Contemporary risk of surgery in patients with ulcerative colitis and Crohn’s disease: a meta‒analysis of population‒based cohorts. Clin Gastroenterol Hepatol. 2021;19:2031-2045. e11. 17) Waljee AK, Wallace BI, Cohen‒Mekelburg S, et al. Development and validation of machine learning models in prediction of remission in patients with moderate to severe Crohn disease. JAMA Network Open. 2019;2:e193721. 10) Takenaka K, Fujii T, Kawamoto A, et al. Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study. Lancet Gastroenterol Hepatol. 2022;7:230-237. 13) Bossuyt P, Nakase H, Vermeire S, et al. Automatic computer-aided determination of endoscopic and histological inflammation in patients with mild to moderate ulcerative colitis based on red density. Gut. 2020;69:1778-1786. 6) Maeda Y, Kudo SE, Mori Y, et al. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). Gastrointest Endosc. 2019;89:408-415. 7) Maeda Y, Kudo SE, Ogata N, et al. Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study. Gastrointest Endosc. 2022;95:747-756. e2. 12) Honzawa Y, Matsuura M, Higuchi H, et al. A novel endoscopic imaging system for quantitative evaluation of colonic mucosal inflammation in patients with quiescent ulcerative colitis. Endosc lnt Open. 2020;8:E41-E49. 18) Miyoshi J, Maeda T, Matsuoka K, et al. Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis. Sci Rep. 2021;11:16440. 4) Travis SP, Schnell D, Krzeski P, et al. Developing an instrument to assess the endoscopic severity of ulcerative colitis: the ulcerative colitis endoscopic index of severity (UCEIS). Gut. 2012;61:535-542. 11) Ogata N, Maeda Y, Misawa M, et al. Artificial intelligence-assisted video colonoscopy for disease monitoring of ulcerative colitis: a prospective study. J Crohns Colitis. 2025;19:jjae080. 1) Neurath MF, Travis SP. Mucosal healing in inflammatory bowel diseases: a systematic review. Gut. 2012;61:1619-1635. 14) Takabayashi K, Kobayashi T, Matsuoka K, et al. Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale. Dig Endosc. 2024;36:582-590. 16) 久松理一,平井郁仁,芦塚伸也,ほか.潰瘍性大腸炎・クローン病診断基準・治療指針.厚生労働科学研究費補助金 難治性疾患政策研究事業「難治性炎症性腸管障害に関する調査研究」(久松班)令和5年度分担研究報告書.令和5年度改訂版.令和6年3月31日 2) Turner D, Ricciuto A, Lewis A, et al. STRIDE-Ⅱ: an update on the selecting therapeutic targets in inflammatory bowel disease (STRIDE) initiative of the international organization for the study of IBD (IOIBD): determining therapeutic goals for treat-to-target strategies in IBD. Gastroenterology. 2021;160:1570-1583. |
References_xml | – reference: 2) Turner D, Ricciuto A, Lewis A, et al. STRIDE-Ⅱ: an update on the selecting therapeutic targets in inflammatory bowel disease (STRIDE) initiative of the international organization for the study of IBD (IOIBD): determining therapeutic goals for treat-to-target strategies in IBD. Gastroenterology. 2021;160:1570-1583. – reference: 9) Takenaka K. Ohtsuka K, Fujii T, et al. Development and validation of a deep neural network for accurate evaluation of endoscopic images from patients with ulcerative colitis. Gastroenterology. 2020;158:2150-2157. – reference: 4) Travis SP, Schnell D, Krzeski P, et al. Developing an instrument to assess the endoscopic severity of ulcerative colitis: the ulcerative colitis endoscopic index of severity (UCEIS). Gut. 2012;61:535-542. – reference: 15) Tsai L, Ma C, Dulai PS, et al. Contemporary risk of surgery in patients with ulcerative colitis and Crohn’s disease: a meta‒analysis of population‒based cohorts. Clin Gastroenterol Hepatol. 2021;19:2031-2045. e11. – reference: 6) Maeda Y, Kudo SE, Mori Y, et al. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). Gastrointest Endosc. 2019;89:408-415. – reference: 17) Waljee AK, Wallace BI, Cohen‒Mekelburg S, et al. Development and validation of machine learning models in prediction of remission in patients with moderate to severe Crohn disease. JAMA Network Open. 2019;2:e193721. – reference: 14) Takabayashi K, Kobayashi T, Matsuoka K, et al. Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale. Dig Endosc. 2024;36:582-590. – reference: 7) Maeda Y, Kudo SE, Ogata N, et al. Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study. Gastrointest Endosc. 2022;95:747-756. e2. – reference: 16) 久松理一,平井郁仁,芦塚伸也,ほか.潰瘍性大腸炎・クローン病診断基準・治療指針.厚生労働科学研究費補助金 難治性疾患政策研究事業「難治性炎症性腸管障害に関する調査研究」(久松班)令和5年度分担研究報告書.令和5年度改訂版.令和6年3月31日. – reference: 11) Ogata N, Maeda Y, Misawa M, et al. Artificial intelligence-assisted video colonoscopy for disease monitoring of ulcerative colitis: a prospective study. J Crohns Colitis. 2025;19:jjae080. – reference: 1) Neurath MF, Travis SP. Mucosal healing in inflammatory bowel diseases: a systematic review. Gut. 2012;61:1619-1635. – reference: 18) Miyoshi J, Maeda T, Matsuoka K, et al. Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis. Sci Rep. 2021;11:16440. – reference: 5) Travis SP, Schnell D, Krzeski P, et al. Reliability and initial validation of the ulcerative colitis endoscopic index of severity. Gastroenterology. 2013;145:987-995. – reference: 3) Schroeder KW, Tremaine WJ, Ilstrup DM. Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. a randomized study. N Engl J Med. 1987;317:1625-1629. – reference: 12) Honzawa Y, Matsuura M, Higuchi H, et al. A novel endoscopic imaging system for quantitative evaluation of colonic mucosal inflammation in patients with quiescent ulcerative colitis. Endosc lnt Open. 2020;8:E41-E49. – reference: 13) Bossuyt P, Nakase H, Vermeire S, et al. Automatic computer-aided determination of endoscopic and histological inflammation in patients with mild to moderate ulcerative colitis based on red density. Gut. 2020;69:1778-1786. – reference: 10) Takenaka K, Fujii T, Kawamoto A, et al. Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study. Lancet Gastroenterol Hepatol. 2022;7:230-237. – reference: 8) Kuroki T, Maeda Y, Kudo SE, et al. A novel artificial intelligence-assisted “vascular-healing” diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video). Gastrointest Endosc. 2024;100:97-108. |
SSID | ssib058744990 ssib058754845 ssib059973352 |
Score | 1.9785627 |
Snippet | ... |
SourceID | jstage |
SourceType | Publisher |
StartPage | 193 |
SubjectTerms | 人工知能 炎症性腸疾患 粘膜治癒 |
Title | 炎症性腸疾患の診断と治療におけるAIの現状 |
URI | https://www.jstage.jst.go.jp/article/jsmu/85/3/85_193/_article/-char/ja |
Volume | 85 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
ispartofPNX | 昭和医科大学雑誌, 2025, Vol.85(3), pp.193-198 |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NaxQxFB9qvXgRRcVvejDHXXcyyUyCp2Q7pRXqqYXehsxMFlywSt29eBB3QYQKipeKZw8WRBEPIgr9ZxZ2-2f4XjLbnUUPdaGE17xf8l7ey868ZPIRBHeobJmYF52GLZlsMJPDc7CQRaNjIVYoSslNhJuTNx_E69vs_g7fWTrzrbZqqd_Lm8Wzf-4rWcSrkAd-xV2y_-HZk0ohA2jwL6TgYUhP5WOSJkRQIlIkJNAJSWNcu6CAEERwooVjxUSnjkWJikgaQfxIVIoYJYhWyAKMWp2yBOZoSrR2xSUWrFi6IoSqCBk6AtTQaqNWN6iz5sQmiNVxPQx2AoUTyImEom0kIPUCQX2slRPFXFM4IlVMUkmkdiwoqwA_7S4I0S0UiARgKXYdV6e3ASMaGqFneJCfoskcLJ6r31_u42FgRE0Ud_hVlOzwzh6I98LiGZ6hNCGd-VJUv1IDlXN4tAmtT7X4DdnuZ7GoVepE5DwEarSJVLOnPE38PKyfVbH1PD73mqoa_7A-i-HeOaG_YrIKX0J_qfdfb0YmI1xL2n36qN8UvBllJ8XmThuv-nKGuEzwLMIEoNmUgZsBsy6MSM7SJAlxEe3m83T6BOd4mYKcfXeF_2FEPPtuzaVMcJMf3vg4bXd1kirqd3dOO4gJuzBCmq6udAHf1oXgfDVSW1Feo4vBUtdcCu5Nhm8m71-NX3w6fvlzcnA0Hn4cDb4eH_4aH3wZDQ7H339PPgxHg8-jwf5o8G40fK02gD95ezTZ_3E52F5Lt9rrjer-kUY3TIRp0DAS1sbwhyfVGcvDiLESz_izpWnRxLAkZ9KyQkShEYUp8g4tS0hNp7Q0N9GVYHn38a69Gqx0WEJL_KJf5niaVC4BLo2lYVzE3NroWpD4tmZP_CEz2WkdcX3hkjeCc9jJ_XzizWC5t9e3tyDC7uW3nVP_AHQTqP8 |
linkProvider | ISSN International Centre |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E7%82%8E%E7%97%87%E6%80%A7%E8%85%B8%E7%96%BE%E6%82%A3%E3%81%AE%E8%A8%BA%E6%96%AD%E3%81%A8%E6%B2%BB%E7%99%82%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8BAI%E3%81%AE%E7%8F%BE%E7%8A%B6&rft.jtitle=%E6%98%AD%E5%92%8C%E5%8C%BB%E7%A7%91%E5%A4%A7%E5%AD%A6%E9%9B%91%E8%AA%8C&rft.au=%E5%B0%8F%E5%BD%A2%2C+%E5%85%B8%E4%B9%8B&rft.au=%E6%9E%97%2C+%E6%AD%A6%E9%9B%85&rft.au=%E8%8B%A5%E6%9D%91%2C+%E9%82%A6%E5%BD%A6&rft.au=%E4%B8%89%E6%BE%A4%2C+%E5%B0%86%E5%8F%B2&rft.date=2025&rft.pub=%E6%98%AD%E5%92%8C%E5%8C%BB%E7%A7%91%E5%A4%A7%E5%AD%A6%E5%AD%A6%E5%A3%AB%E4%BC%9A&rft.issn=2759-8144&rft.eissn=2759-8152&rft.volume=85&rft.issue=3&rft.spage=193&rft.epage=198&rft_id=info:doi/10.14930%2Fjsmu.85.3_193&rft.externalDocID=article_jsmu_85_3_85_193_article_char_ja |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2759-8144&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2759-8144&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2759-8144&client=summon |