影像组学和深度学习在房颤检测中的研究进展

马 玉花1, 马玉 兰*2
1、青海大学
2、青海省心脑血管病专科医院

摘要


心房颤动（AF）是一种常见的心律失常，其导致认知障碍、脑卒中、心力衰竭及过早死亡的风险较高。导管消融是恢复正常窦性心律的关键替代药物治疗。尽管对房颤发病机制的了解有所进展，但大约35%的患者在导管消融后12个月房颤复发。因此，准确预测导管消融术后房颤复发对患者的诊疗具有重要意义。此外，随着我国老龄化的进展，心房颤动将给社会带来沉重负担，因此，AF的诊断和治疗是至关重要的。目前，各种机器学习和深度学习（DL）技术已经开发用于医学领域，例如，影像组学，这一新兴的领域，在肿瘤学中在改善诊断、癌症分期和分级以及改善个性化治疗方面重要性日益增加，这一点已经得到了充分的证实。影像组学分析亦已开发用于自动检测AF、判断其复发等方面，从医学图像如心电图、胸部CT、冠脉CTA、心脏CT中提取并量化放射组学特征，通过提供具有独立预测能力的数据和识别数据中的重要关系来增强房颤检出以及复发预测，本文综述了提取以及分析影像组学的各种方法及各类深度学习模型，分析表明，大多数研究使用CNN模型，该模型在AF预测及复发预测方面产生了较高的检测性能。由于缺乏标准化的参数获取，放射学方法不一致，缺乏外部验证以及读者的知识和经验不同，影像组学仍未应用于临床常规，但该综述可作为参考，帮助研究人员进一步探索AF检测相关领域。

关键词


心房颤动；影像组学；深度学习

全文:

PDF


参考


[1]MARKIDES V, SCHILLING R J. Atrial fibrillation: classification, pathophysiology, mechanisms and drug treatment [J]. HEART, 2003, 89(8): 939-43.

[2]CAMM A J, LIP G Y H, DE CATERINA R, et al. 2012 focused update of the ESC Guidelines for the management of atrial fibrillation An update of the 2010 ESC Guidelines for the management of atrial fibrillation Developed with the special contribution of the European Heart Rhythm Association [J]. EUROPACE, 2012, 14(10): 1385-413.

[3]SHUR J D, DORAN S J, KUMAR S, et al. Radiomics in Oncology: A Practical Guide [J]. Radiographics, 2021, 41(6): 1717-32.

[4]JANUARY C T, WANN L S, ALPERT J S, et al. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society [J]. Circulation, 2014, 130(23): 2071-104.

[5]FAN X, YAO Q, CAI Y, et al. Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings [J]. IEEE J Biomed Health Inform, 2018, 22(6): 1744-53.

[6]XIA Y, WULAN N, WANG K, et al. Detecting atrial fibrillation by deep convolutional neural networks [J]. Comput Biol Med, 2018, 93: 84-92.

[7]RUBIN J, PARVANEH S, RAHMAN A, et al. Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings [J]. J Electrocardiol, 2018, 51(6s): S18-s21.

[8]JO Y Y, CHO Y, LEE S Y, et al. Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram [J]. Int J Cardiol, 2021, 328: 104-10.

[9]CHEN X, CHENG Z, WANG S, et al. Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals [J]. Comput Methods Programs Biomed, 2021, 202: 106009.

[10]ACHARYA U R, FUJITA H, LIH O S, et al. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network [J]. Information Sciences, 2017, 405: 81-90.

[11]POLIDORI T, DE SANTIS D, RUCCI C, et al. Radiomics applications in cardiac imaging: a comprehensive review [J]. RADIOLOGIA MEDICA, 2023, 128(8): 922-33.

[12]ZHANG Z, DING Y, LIN K, et al. Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysis [J]. EClinicalMedicine, 2025, 81: 103118.

[13]D’ASCENZO F, CORLETO A, BIONDI-ZOCCAI G, et al. Which are the most reliable predictors of recurrence of atrial fibrillation after transcatheter ablation?: a meta-analysis [J]. Int J Cardiol, 2013, 167(5): 1984-9.

[14]MIAO Y, XU M, ZHANG C, et al. An echocardiographic model for predicting the recurrence of paroxysmal atrial fibrillation after circumferential pulmonary vein ablation [J]. Clin Cardiol, 2021, 44(11): 1506-15.

[15]HORDE G W, AYYALA D, MADDUX P, et al. Creation and Validation of an Algorithm for Predicting the Recurrence of Atrial Fibrillation Following Pulmonary Vein Isolation by Utilizing Real-World Data and Ensemble Modeling Techniques [J]. Cureus, 2023, 15(8): e43234.

[16]TRUONG E T, LYU Y, IHDAYHID A R, et al. Beyond Clinical Factors: Harnessing Artificial Intelligence and Multimodal Cardiac Imaging to Predict Atrial Fibrillation Recurrence Post-Catheter Ablation [J]. JOURNAL OF CARDIOVASCULAR DEVELOPMENT AND DISEASE, 2024, 11(9).

[17]TANG S, RAZEGHI O, KAPOOR R, et al. Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes [J]. Circ Arrhythm Electrophysiol, 2022, 15(8): e010850.

[18]HWANG Y T, LEE H L, LU C H, et al. A Novel Approach for Predicting Atrial Fibrillation Recurrence After Ablation Using Deep Convolutional Neural Networks by Assessing Left Atrial Curved M-Mode Speckle-Tracking Images [J]. Front Cardiovasc Med, 2020, 7: 605642.

[19]JALTOTAGE B, IHDAYHID A R, LAN N S R, et al. Artificial Intelligence in Cardiology: An Australian Perspective [J]. Heart Lung Circ, 2023, 32(8): 894-904.


Refbacks

  • 当前没有refback。