心臓 55/12 2023年12月号

出版社: 日本医学出版
発行日: 2023-12-15
分野: 臨床医学:内科  >  雑誌
ISSN: 05864488
雑誌名:
電子書籍版: 2023-12-15
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目次

  • Open HEART
     知的好奇心と研究

    HEART’s Selection
     循環器疾患とAI
     1.レントゲンの新時代:AIが実臨床で拓く可能性
     2.心電図AIの力:心電図AIが循環器診療を革新する
     3.心エコーAIの挑戦:希少疾患検出への新たなアプローチ
     4.虚血性心疾患診療の革新:AIが描くプレシジョンメディスンの可能性
     5.循環器医の新たなパートナー:ChatGPTの活用法とその効用

    [連載]HEART's Column
    Authorが語る日本発のクリニカル・エビデンス:論文の解説と発表後の展望
     Long-term outcome of chronic thromboembolic pulmonary hypertension using direct oral
     anticoagulants and warfarin:a Japanese prospective cohort study

    [連載]HEART’s Up To Date
    循環器医が知っておくべき最新のエビデンス

    HEART’s Original
    [症例]
     三尖弁輪–下大静脈峡部ブロックの存在下に、通常型心房粗動に
      極めて酷似する心電図波形を呈した時計方向僧帽弁旋回性心房粗動の1例
    [症例]
     乳癌に対する化学療法中に発症したG-CSF製剤による薬剤性血管炎の1例
    [Editorial Comment]
     岩本論文に対するEditorial Comment
    [症例]
     魚骨による食道心嚢瘻から細菌性心膜炎に至った1例
    [Editorial Comment]
     高柳論文に対するEditorial Comment
    [症例]
     特発性血小板減少性紫斑病を合併した非ST上昇型心筋梗塞患者に対して
      超短期DAPTで加療し良好な経過であった1例
    [Editorial Comment]
     吉田論文に対するEditorial Comment―出血ハイリスク(HBR)患者におけるDAPTの治療戦略
    [Editorial Comment]
     吉田論文に対するEditorial Comment
    [症例]
     挙児希望のある連合弁膜症患者に生体弁による大動脈弁置換術と僧帽弁交連切開術を施行した1例

この書籍の参考文献

参考文献のリンクは、リンク先の都合等により正しく表示されない場合がありますので、あらかじめご了承下さい。

本参考文献は電子書籍掲載内容を元にしております。

【HEART' s Selection】

P.1134 掲載の参考文献
1) Kusunose K : Steps to use artificial intelligence in echocardiography. J Echocardiogr 2021 ; 19 : 21-27
2) Kusunose K : Revolution of echocardiographic reporting : the new era of artificial intelligence and natural language processing. J Echocardiogr 2023 ; 21 : 99-104
3) Kusunose K, Hirata Y, Tsuji T, et al : Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray. Sci Rep 2020 ; 10 : 19311
4) Hirata Y, Kusunose K, Tsuji T, et al : Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-Ray. Can J Cardiol 2021 ; 37 : 1198-1206
5) Kusunose K, Hirata Y, Yamaguchi N, et al : Deep learning approach for analyzing chest x-rays to predict cardiac events in heart failure. Front Cardiovasc Med 2023 ; 10 : 1081628
P.1138 掲載の参考文献
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4) Katsushika S, Kodera S, Nakamoto M, et al : The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms. Int Heart J 2021 ; 62 : 1332-1341
5) Selvaraju RR, Cogswell M, Das A, et al : Grad-CAM : Visual Explanations from Deep Networks via Gradient-based Localization. In : Proc IEEE Int Conf Comput Vis ; 2017, p.618-626
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7) Katsushika S, Kodera S, Sawano S, et al : An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced left ventricular function. Eur Heart J Digit Health 2023 ; 4 : 254-264
8) O'Neal WT, Mazur M, Bertoni AG, et al : Electrocardiographic predictors of heart failure with reduced versus preserved ejection fraction : The multi-ethnic study of atherosclerosis. J Am Heart Assoc 2017 ; 6 : e006023
9) 小寺聡, 仁宮洸太, 澤野晋之介, ほか : 医療AIに対する患者の意識調査. 人工知能学会全国大会論文集 2022. doi : https://doi.org/10.11517/pjsai.JSAI2022.0_2K6OS1b04 (cited 2023 Oct 08)
P.1143 掲載の参考文献
1) Goto S, Goto S : Application of Neural Networks to 12-Lead Electrocardiography-Current Status and Future Directions-. Circ Rep 2019 ; 1 : 481-486
2) Goto S, McGuire DK, Goto S : The Future Role of High-Performance Computing in Cardiovascular Medicine and Science -Impact of Multi-Dimensional Data Analysis. J Atheroscler Thromb 2022 ; 29 : 559-562
3) Deo RC : Machine Learning in Medicine. Circulation 2015 ; 132 : 1920-1930
4) Shiraishi Y, Goto S, Niimi N, et al : Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography. Europace 2023 ; 25 : 922-930
5) Goto S, Goto S, Pieper KS, et al : New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists : GARFIELD-AF. Eur Heart J Cardiovasc Pharmacother 2019 ; 6 : 301-309
6) Miura K, Goto S, Katsumata Y, et al : Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data, NPJ Digit Med 2020 ; 3 : 141
7) Goto S, Kimura M, Katsumata Y, et al : Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients, PLoS One 2019 ; 14 : e0210103
8) Goto S, Ozawa H : The Importance of External Validation for Neural Network Models. JACC Advances 2023 ; 2 : 100610
9) Goto S, Mahara K, Beussink-Nelson L, et al : Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms, Nat Commun 2021 ; 12 : 2726
10) Goto S, Solanki D, John JE, et al : Multinational Federated Learning Approach to Train ECG and Echocardiogram Models for Hypertrophic Cardiomyopathy Detection. Circulation 2022 ; 146 : 755-769
11) Miura K, Yagi R, Miyama H, et al : Deep learning-based model detects atrial septal defects from electrocardiography : a cross-sectional multicenter hospital-based study, EClinicalMedicine 2023 ; 63 : 102141
12) Yagi R, Goto S, Katsumata Y, et al : Importance of external validation and subgroup analysis of artificial intelligence in the detection of low ejection fraction from electrocardiograms, Eur Heart J Digit Health 2022 ; 3 : 654-657
P.1148 掲載の参考文献
1) Patel AP, Khera AV : Advances and Applications of Polygenic Scores for Coronary Artery Disease. Annu Rev Med 2023 ; 74 : 141-154
2) Khera AV, Emdin CA, Drake I, et al : Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease. N Engl J Med 2016 ; 375 : 2349-2358
3) Mega JL, Stitziel NO, Smith JG, et al : Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy : an analysis of primary and secondary prevention trials. Lancet 2015 ; 385 : 2264-2271
4) Mujwara D, Henno G, Vernon ST, et al : Integrating a Polygenic Risk Score for Coronary Artery Disease as a Risk-Enhancing Factor in the Pooled Cohort Equation : A Cost-Effectiveness Analysis Study. J Am Heart Assoc 2022 ; 11 : e025236
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6) Steinhubl SR, Waalen J, Edwards AM, et al : Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation : The mSToPS Randomized Clinical Trial. JAMA 2018 ; 320 : 146-155
7) Perez MV, Mahaffey KW, Hedlin H, et al : Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med 2019 ; 381 : 1909-1917
8) Al-Zaiti SS, Martin-Gill C, Zegre-Hemsey JK, et al : Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med 2023 ; 29 : 1804-1813
9) Jiang LY, Liu XC, Nejatian NP, et al : Health system-scale language models are all-purpose prediction engines. Nature 2023 ; 619 : 357-362
10) Tu T, Azizi S, Driess D, et al : Towards generalist biomedical AI. arXiv preprint 2023 Jul 26 ; arXiv : 2307. 14334. doi : https://doi.org/10.48550/arXiv.2307.14334 (cited 2023 Oct 10)
P.1154 掲載の参考文献
1) Vaswani A, Shazeer N, Parmar N, et al : Attention is All you Need. Advances in Neural Information Processing Systems 30 (NIPS 2017)
2) Brown T, Mann B, Ryder N, et al : Language models are few-shot learners. Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
3) Ouyang L, Wu J, Jiang X, et al : Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
4) Kung TH, Cheatham M, Medenilla A, et al : Performance of ChatGPT on USMLE : Potential for AI-assisted medical education using large language models. PLOS Digit Health 2023 ; 2 : e0000198
5) Tanaka Y, Nakata T, Aiga K, et al : Performance of Generative Pretrained Transformer on the National Medical Licensing Examination in Japan. medRxiv 2023.Apr.17. doi : https://doi.org/10.1101/2023.04.17.23288603 (cited 2023 Sep 23)
6) Doximity Inc. : DocsGPT. https://www.doximity.com/docs-gpt-info/ (cited 2023 Sep 23)
7) Glass Health : Glass AI. https://glass.health/ai/ (cited 2023 Sep 23)

【連載】

P.1157 掲載の参考文献
1) Hosokawa K, Abe K, Funakoshi K, Tamura Y, Nakashima N, Todaka K, Taniguchi Y, Inami T, Adachi S, Tsujino I, Yamashita J, Minatsuki S, Ikeda N, Shimokawahara H, Kawakami T, Ogo T, Hatano M, Ogino H, Fukumoto Y, Tanabe N, Matsubara H, Fukuda K, Tatsumi K, Tsutsui H : Long-term outcome of chronic thromboembolic pulmonary hypertension using direct oral anticoagulants and warfarin : a Japanese prospective cohort study. J Thromb Haemost 2023 ; 21 : 2151-2162
2) Mizoguchi H, Ogawa A, Munemasa M, et al : Refined balloon pulmonary angioplasty for inoperable patients with chronic thromboembolic pulmonary hypertension. Circ Cardiovasc Interv 2012 ; 5 : 748-755
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5) Hosokawa K, Abe K, Kishimoto J, et al : Efficacy and safety of edoxaban in patients with chronic thromboembolic pulmonary hypertension : protocol for a multicentre, randomised, warfarin-controlled, parallel group trial-KABUKI trial. BMJ Open 2022 ; 12 : e061225
6) Hosokawa K, Watanabe H, Taniguchi Y, et al : A multicenter, single-blind, randomized, warfarin-controlled trial of edoxaban in patients with chronic thromboembolic pulmonary hypertension : KABUKI trial. Circulation 2023 Nov 13. doi : 10.1161/CIRCULATIONAHA.123.067528. [Online ahead of print]

【HEART' s Original】

P.1167 掲載の参考文献
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P.1175 掲載の参考文献
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P.1193 掲載の参考文献
1) Watanabe H, Domei T, Morimoto T, et al : Effect of 1-Month Dual Antiplatelet Therapy Followed by Clopidogrel vs 12-Month Dual Antiplatelet Therapy on Cardiovascular and Bleeding Events in Patients Receiving PCI : The STOPDAPT-2 Randomized Clinical Trial. JAMA 2019 ; 321 : 2414-2427
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3) Watanabe H, Morimoto T, Natsuaki M, et al : Comparison of Clopidogrel Monotherapy After 1 to 2 Months of Dual Antiplatelet Therapy With 12 Months of Dual Antiplatelet Therapy in Patients With Acute Coronary Syndrome : The STOPDAPT-2 ACS Randomized Clinical Trial. JAMA Cardiol 2022 ; 7 : 407-417
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P.1200 掲載の参考文献
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