切换至 "中华医学电子期刊资源库"

中华心脏与心律电子杂志 ›› 2023, Vol. 11 ›› Issue (01) : 1 -4. doi: 10.3877/cma.j.issn.2095-6568.2023.01.001

述评

人工智能在心脏疾病诊疗与研究领域的进展与展望
周珊珊1, 陈韵岱1,()   
  1. 1. 100048 北京,中国人民解放军总医院第六医学中心心血管病医学部
  • 收稿日期:2023-03-20 出版日期:2023-03-25
  • 通信作者: 陈韵岱

Progress and future prospect of artificial intelligence in the field of diagnosis, treatment and research of heart diseases

Shanshan Zhou1, Yundai Chen1,()   

  1. 1. Department of Cardiology, The Sixth Medical Centre, Chinese PLA General Hospital,Beijing 100853, China
  • Received:2023-03-20 Published:2023-03-25
  • Corresponding author: Yundai Chen
引用本文:

周珊珊, 陈韵岱. 人工智能在心脏疾病诊疗与研究领域的进展与展望[J/OL]. 中华心脏与心律电子杂志, 2023, 11(01): 1-4.

Shanshan Zhou, Yundai Chen. Progress and future prospect of artificial intelligence in the field of diagnosis, treatment and research of heart diseases[J/OL]. Chinese Journal of Heart and Heart Rhythm(Electronic Edition), 2023, 11(01): 1-4.

人工智能(AI)在心脏病诊疗与研究领域的应用正处于快速发展阶段。从心电图诊断到心脏影像诊断,从风险预测到临床决策支持,AI都已经取得了显著进展。未来,随着数据整合与共享、个性化诊疗、多学科交叉等趋势的发展,AI技术可以协助心脏疾病的诊疗与研究技术与其他学科进行交叉互融,实现多学科之间的深度融合和协同创新,将进一步推动心脏病诊疗的革命性变革。但AI的局限性和不足之处也应该引起我们足够的重视。

[1]
Mincholé A, Rodriguez B. Artificial intelligence for the electrocardiogram[J]. Nat Med, 2019, 25(1):22-23.
[2]
Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram[J]. Nat Med, 2019, 25(1):70-74.
[3]
Halcox J, Wareham K, Cardew A, et al. Assessment of remote heart rhythm sampling using the aliveCor heart monitor to screen for atrial fibrillation: The REHEARSE-AF Study[J]. Circulation, 2017, 136(19):1784-1794.
[4]
Goldenthal IL, Sciacca RR, Riga T, et al. Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results[J]. J Cardiovasc Electrophysiol, 2019, 30(11):2220-2228.
[5]
Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation[J]. N Engl J Med, 2019, 381(20):1909-1917.
[6]
Tison GH, Sanchez JM, Ballinger B, et al. Passive detection of atrial fibrillation using a commercially available smartwatch[J]. JAMA Cardiol, 2018, 3(5):409-416.
[7]
Guo Y, Lane DA, Wang L, et al. Mobile health technology to improve care for patients with atrial fibrillation[J]. J Am Coll Cardiol, 2020,75(13):1523-1534.
[8]
党梦秋, 范嘉祺, 戴晗怡, 等. 智能手表对主动脉瓣置换术后患者心房颤动及左束支传导阻滞的诊断价值[J/OL]. 中华心脏与心律电子杂志, 2023, 11(1): 24-27.
[9]
Oikonomou EK, Marwan M, Desai MY, et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data[J]. Lancet, 2018, 392(10151):929-939.
[10]
Arsanjani R, Xu Y, Dey D, et al. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population[J]. J Nucl Cardiol, 2013, 20(4):553-562.
[11]
Betancur J, Commandeur F, Motlagh M, et al. Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study[J]. JACC Cardiovasc Imaging, 2018, 11(11):1654-1663.
[12]
Hu LH, Betancur J, Sharir T, et al. Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry[J]. Eur Heart J Cardiovasc Imaging, 2020, 21(5):549-559.
[13]
Al'Aref SJ, Anchouche K, Singh G, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging[J]. Eur Heart J, 2019, 40(24):1975-1986.
[14]
Dey D, Gaur S, Ovrehus KA, et al. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study[J]. Eur Radiol, 2018, 28(6):2655-2664.
[15]
Henglin M, Stein G, Hushcha PV, et al. Machine learning approaches in cardiovascular imaging[J]. Circ Cardiovasc Imaging, 2017,10(10):e005614.
[16]
Krittanawong C, Virk H, Bangalore S, et al. Machine learning prediction in cardiovascular diseases: a meta-analysis[J]. Sci Rep, 2020, 10(1):16057.
[17]
Al'Aref SJ, Maliakal G, Singh G, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry[J]. Eur Heart J, 2020, 41(3):359-367.
[18]
沈安娜, 李建新, 刘芳超, 等. 静息心率对10年心血管疾病风险的影响[J/OL]. 中华心脏与心律电子杂志, 2023, 11(1): 5-11.
[19]
Bai W, Sinclair M, Tarroni G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks[J]. J Cardiovasc Magn Reson, 2018, 20(1):65.
[20]
Bhuva AN, Bai W, Lau C, et al. A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis[J]. Circ Cardiovasc Imaging, 2019, 12(10):e009214.
[21]
Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database[J]. NPJ Digit Med, 2020, 3:118.
[22]
Chiarito M, Luceri L, Oliva A, et al. Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold[J]. Eur Cardiol, 2022, 17:e29.
[1] 李洋, 蔡金玉, 党晓智, 常婉英, 巨艳, 高毅, 宋宏萍. 基于深度学习的乳腺超声应变弹性图像生成模型的应用研究[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 563-570.
[2] 杨敬武, 周美君, 陈雨凡, 李素淑, 何燕妮, 崔楠, 刘红梅. 人工智能超声结合品管圈活动对低年资超声医师甲状腺结节风险评估能力的作用[J/OL]. 中华医学超声杂志(电子版), 2024, 21(05): 522-526.
[3] 明昊, 肖迎聪, 巨艳, 宋宏萍. 乳腺癌风险预测模型的研究现状[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(05): 287-291.
[4] 黄鸿初, 黄美容, 温丽红. 血液系统恶性肿瘤患者化疗后粒细胞缺乏感染的危险因素和风险预测模型[J/OL]. 中华实验和临床感染病杂志(电子版), 2024, 18(05): 285-292.
[5] 叶莉, 杜宇. 深度学习在牙髓根尖周病临床诊疗中的应用[J/OL]. 中华口腔医学研究杂志(电子版), 2024, 18(06): 351-356.
[6] 熊鹰, 林敬莱, 白奇, 郭剑明, 王烁. 肾癌自动化病理诊断:AI离临床还有多远?[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 535-540.
[7] 李伟, 宋子健, 赖衍成, 周睿, 吴涵, 邓龙昕, 陈锐. 人工智能应用于前列腺癌患者预后预测的研究现状及展望[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 541-546.
[8] 黄俊龙, 李文双, 李晓阳, 刘柏隆, 陈逸龙, 丘惠平, 周祥福. 基于盆底彩超的人工智能模型在女性压力性尿失禁分度诊断中的应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 597-605.
[9] 莫淇舟, 苏劲, 黄健, 李健维, 李思宁, 柳建军. 智能控压输尿管软镜碎石吸引取石术在直径10~25 mm上尿路结石中的应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(05): 497-502.
[10] 苏博兴, 肖博, 李建兴. 2024年美国泌尿外科学会年会结石领域手术治疗相关热点研究及解读[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(04): 303-308.
[11] 莫林键, 杨舒博, 农卫赟, 程继文. 人工智能虚拟数字医师在钬激光前列腺剜除日间手术患教管理中的应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(04): 318-322.
[12] 李义亮, 苏拉依曼·牙库甫, 麦麦提艾力·麦麦提明, 克力木·阿不都热依木. 机器人与腹腔镜食管裂孔疝修补术联合Nissen 胃底折叠术短期疗效分析[J/OL]. 中华疝和腹壁外科杂志(电子版), 2024, 18(05): 512-517.
[13] 王石林, 叶继章, 丘向艳, 陈桂青, 邹晓敏. 慢性阻塞性肺疾病真菌感染风险早期预测分析[J/OL]. 中华肺部疾病杂志(电子版), 2024, 17(05): 773-776.
[14] 蔡晓雯, 李慧景, 丘婕, 杨翼帆, 吴素贤, 林玉彤, 何秋娜. 肝癌患者肝动脉化疗栓塞术后疼痛风险预测模型的构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 722-728.
[15] 孙铭远, 褚恒, 徐海滨, 张哲. 人工智能应用于多发性肺结节诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 785-790.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?