机器学习在心血管疾病辅助诊断中的研究: 现状与未来进展
PDF下载 (202)王 丽,计礼丽,陆浩轩,谢燕青,何文明.机器学习在心血管疾病辅助诊断中的研究: 现状与未来进展[J].宁波大学学报(理工版),2023,36(3):102-107.DOI:
WANG Li,JI Lili,LU Haoxuan,XIE Yanqing,HE Wenming.Research in machine learning for auxiliary diagnosis of cardiovascular diseases: Current status and future prospects[J].Journal of Ningbo University(Natural Science & Engineering Edition),2023,36(3):102-107.DOI:
| Title: | Research in machine learning for auxiliary diagnosis of cardiovascular diseases: Current status and future prospects |
| 作者: | 王 丽, 计礼丽, 陆浩轩, 谢燕青, 何文明 |
| Author(s): | WANG Li, JI Lili, LU Haoxuan, XIE Yanqing, HE Wenming |
| 关键词: | 心血管疾病; 机器学习; 人工智能; 辅助诊断 |
| Keywords: | cardiovascular diseases; machine learning; artificial intelligence; auxiliary diagnosis |
| 分类号: | R541 |
| 文献标识码: | A |
| 摘要: | 心血管疾病具有高发病率、高住院率、高致残率、高死亡率等特征, 伴随高额的治疗疾病负担, 早期准确诊断心血管疾病意义重大. 以机器学习(ML)为代表的人工智能心血管疾病辅助诊断技术为诊断心血管疾病提供了新方法. 基于ML疾病诊断技术日趋成熟, 且在各类心血管疾病中取得诸多进展, 包括冠心病、心力衰竭及心律失常等. 本文综述了ML在心血管疾病辅助诊断中的技术背景和研究现状, 分析了该领域在临床转化应用方面的挑战, 并对未来研究进行了展望. |
| Abstract: | Cardiovascular diseases are characterized by high morbidity, hospitalization, disability, and mortality rates, accompanied with a high disease burden. Therefore, early and accurate diagnosis of cardiovascular diseases is of great significance. The artificial intelligence cardiovascular disease aided diagnosis technology represented by machine learning (ML) provides a new approach to solve the above problems. ML-based disease diagnosis techniques are becoming increasingly mature and have advantages in various cardiovascular diseases, including heart failure, coronary atherosclerotic heart disease, and arrhythmias. This paper reviews the technical background and current research development of ML in the ancillary diagnosis of cardiovascular diseases. It also analyzes the challenges and prospects for translational clinical applications in this field. |
| 参考文献 /References: | [1] Roth G A, Mensah G A, Johnson C O, et al. Global burden of cardiovascular diseases and risk factors, 1990- 2019[J]. Journal of the American College of Cardiology, 2020, 76(25):2982-3021. [2] Mela A, Rdzanek E, Poniatowski ? A, et al. Economic costs of cardiovascular diseases in Poland estimates for 2015- 2017 years[J]. Frontiers in Pharmacology, 2020, 11:1231. [3] Mathur P, Srivastava S, Xu X W, et al. Artificial intelligence, machine learning, and cardiovascular disease [J]. Clinical Medicine Insights: Cardiology, 2020, 14: 1179546820927404. [4] 赵梦蝶, 孙九爱. 机器学习在心血管疾病诊断中的研究进展[J]. 北京生物医学工程, 2020, 39(2):208-214. [5] Lüscher T F, Lyon A, Amstein R, et al. Artificial intelligence: The pathway to the future of cardiovascular medicine[J]. European Heart Journal, 2022, 43(7):556-558. [6] Leeming J. How AI is helping the natural sciences[J]. Nature, 2021, 598(7880):S5-S7. [7] Fernández-Ruiz I. Artificial intelligence to improve the diagnosis of cardiovascular diseases[J]. Nature Reviews Cardiology, 2019, 16(3):133. [8] Attia Z I, Harmon D M, Behr E R, et al. Application of artificial intelligence to the electrocardiogram[J]. European Heart Journal, 2021, 42(46):4717-4730. [9] Greener J G, Kandathil S M, Moffat L, et al. A guide to machine learning for biologists[J]. Nature Reviews Molecular Cell Biology, 2022, 23(1):40-55. [10] Jiang T, Gradus J L, Rosellini A J. Supervised machine learning: A brief primer[J]. Behavior Therapy, 2020, 51(5):675-687. [11] Eckhardt C M, Madjarova S J, Williams R J, et al. Unsupervised machine learning methods and emerging applications in healthcare[J]. Knee Surgery, Sports Traumatology, Arthroscopy, 2023, 31(2):376-381. [12] Wang X H, Li J, Sun J, et al. A machine learning model based on unsupervised clustering multihabitat to predict the pathological grading of meningiomas[J]. BioMed Research International, 2022, 2022:8955227. [13] Ezugwu A E, Ikotun A M, Oyelade O O, et al. A comprehensive survey of clustering algorithms: State- of-the-art machine learning applications, taxonomy, challenges, and future research prospects[J]. Engineering Applications of Artificial Intelligence, 2022, 110:104743. [14] Nadakinamani R G, Reyana A, Kautish S, et al. Clinical data analysis for prediction of cardiovascular disease using machine learning techniques[J]. Computational Intelligence and Neuroscience, 2022, 2022:1-13. [15] Shehzadi S, Hassan M A, Rizwan M, et al. Diagnosis of chronic ischemic heart disease using machine learning techniques[J]. Computational Intelligence and Neuroscience, 2022, 2022:3823350. [16] Kim J, Lee S Y, Cha B H, et al. Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease[J]. Frontiers in Cardiovascular Medicine, 2022, 9:933803. [17] Jin X, Li Y Z, Yan F, et al. Automatic coronary plaque detection, classification, and stenosis grading using deep learning and radiomics on computed tomography angiography images: A multi-center multi-vendor study [J]. European Radiology, 2022, 32(8):5276-5286. [18] 贾蕾, 赵文兴, 王鑫, 等. CCTA冠周FAI联合上胸部补充扫描与图像大视野重建在冠心病筛查中的应用[J]. 影像科学与光化学, 2022, 40(6):1438-1443. [19] Khozeimeh F, Sharifrazi D, Izadi N H, et al. RF-CNN-F: Random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance[J]. Scientific Reports, 2022, 12(1):1-12. [20] 王华, 李莹莹. 2022年AHA/ACC/HFSA心力衰竭管理指南解读——从新指南看心衰分类和诊断评估[J]. 中国心血管病研究, 2022, 20(6):481-486. [21] Asch F M, Nicolas P, Theodore A, et al. Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert[J]. Circulation: Cardiovascular Imaging, 2019, 12(9):e009303. [22] Asch F M, Mor-Avi V, Rubenson D, et al. Deep learning- based automated echocardiographic quantification of left ventricular ejection fraction: A point-of-care solution[J]. Circulation Cardiovascular Imaging, 2021, 14(6):e012293. [23] Kwon J M, Kim K H, Jeon K H, et al. Development and validation of deep-learning algorithm for electro- cardiography-based heart failure identification[J]. Korean Circulation Journal, 2019, 49(7):629-639. [24] Attia Z I, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram[J]. Nature Medicine, 2019, 25(1):70-74. [25] Kashou A H, Medina-Inojosa J R, Noseworthy P A, et al. Artificial intelligence-augmented electrocardiogram detection of left ventricular systolic dysfunction in the general population[J]. Mayo Clinic Proceedings, 2021, 96(10):2576-2586. [26] Grune J, Yamazoe M, Nahrendorf M. Electroimmunology and cardiac arrhythmia[J]. Nature Reviews Cardiology, 2021, 18(8):547-564. [27] Kersten D J, D’Angelo K, Vargas J, et al. Determining the clinical significance of computer interpreted electro- cardiography conclusions[J]. American Journal of Cardiovascular Disease, 2021, 11(3):375-381. [28] Baek Y S, Lee S C, Choi W, et al. A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm[J]. Scientific Reports, 2021, 11(1):12818. [29] Chang S N, Tseng Y H, Chen J J, et al. An artificial intelligence-enabled ECG algorithm for identifying ventricular premature contraction during sinus rhythm[J]. European Journal of Medical Research, 2022, 27(1):289. [30] Raghunath S, Pfeifer J M, Ulloa-Cerna A E, et al. Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation-related stroke[J]. Circulation, 2021, 143(13):1287-1298. [31] 张明伟, 张天逸, 程云章. 基于1D CNN-BiLSTM网络联合集成学习的心律失常智能诊断系统[J]. 生物医学工程研究, 2022, 41(3):259-267. [32] Sangha V, Mortazavi B J, Haimovich A D, et al. Automated multilabel diagnosis on electrocardiographic images and signals[J]. Nature Communications, 2022, 13(1):1-12. [33] Crea F. The growing role of artificial intelligence and of wearable devices in the management of arrhythmias[J]. European Heart Journal, 2021, 42(38):3889-3893. [34] Whelton P K, Carey R M, Aronow W S, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: A report of the American college of cardiology/ American heart association task force on clinical practice guidelines[J]. Hypertension, 2018, 71(6):1269-1324. [35] Samek W, Wiegand T, Müller K R. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models[EB/OL]. [2023-01-12]. https://www. amazon.com/Explainable-AI-Interpreting-Explaining-Visualizing/dp/3030289532. [36] Silva G F S, Fagundes T P, Teixeira B C, et al. Machine learning for hypertension prediction: A systematic review [J]. Current Hypertension Reports, 2022, 24(11):523-533. [37] 王立娜, 雷警输, 谭琛. 人工智能在高血压诊治中的应用[J]. 实用心电学杂志, 2022, 31(6):386-391. [38] Zheng J, Yu Z Y. A novel machine learning-based systolic blood pressure predicting model[J]. Journal of Nanomaterials, 2021, 2021:1-8. [39] Baker S, Xiang W, Atkinson I. A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms[J]. Computer Methods and Programs in Biomedicine, 2021, 207:106191. [40] Angelaki E, Barmparis G D, Kochiadakis G, et al. Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals [J]. Journal of Hypertension, 2022, 40(12):2494-2501. [41] 杨志云, 王辉, 贺毅, 等. 心肌病心脏磁共振应用的新视角——人工智能和机器学习[J]. 心肺血管病杂志, 2022, 41(3):321-323. [42] Sawano S, Kodera S, Katsushika S, et al. Deep learning model to detect significant aortic regurgitation using electrocardiography[J]. Journal of Cardiology, 2022, 79(3):334-341. [43] Ulloa-Cerna A E, Jing L Y, Pfeifer J M, et al. rECHOmmend: An ECG-based machine learning approach for identifying patients at increased risk of undiagnosed structural heart disease detectable by echocardiography[J]. Circulation, 2022, 146(1):36-47. [44] 谢冰歆, 刘彤. 人工智能心电图诊断模型在心脏瓣膜病识别中的应用[J]. 实用心电学杂志, 2023, 32(1):26-29. [45] Yang F F, Chen X T, Lin X X, et al. Automated analysis of Doppler echocardiographic videos as a screening tool for valvular heart diseases[J]. JACC: Cardiovascular Imaging, 2022, 15(4):551-563. [46] Ueno R, Matsui H, Xu L. Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor[EB/OL]. [2023-01-12]. https://www.ncbi. nlm.nih.gov/pmc/articles/PMC6591226. [47] Khunte M, Chae A, Wang R, et al. Trends in clinical validation and usage of US food and drug administration- cleared artificial intelligence algorithms for medical imaging[J]. Clinical Radiology, 2023, 78(2):123-129. [48] Baptista M L, Goebel K, Henriques E M P. Relation between prognostics predictor evaluation metrics and local interpretability SHAP values[J]. Artificial Intelligence, 2022, 306:103667. [49] Dieber J, Kirrane S. A novel model usability evaluation framework (MUsE) for explainable artificial intelligence [J]. Information Fusion, 2022, 81:143-153. [50] Gao Z H, Lou L H, Wang M H, et al. Application of machine learning in intelligent medical image diagnosis and construction of intelligent service process[J]. Computational Intelligence and Neuroscience, 2022, 2022:9152605. |
| 备注/Memo: | 收稿日期:2023-02-08.宁波大学学报（理工版）网址:http://journallg.nbu.edu.cn/ 基金项目:浙江省“尖兵”“领雁”研发攻关计划科技合作领域项目（2023C04017）;浙江省卫生科技计划（WKJ-ZJ-2137）;浙江省基础公益研究技术项目（LGF22H090008）;宁波市自然科学基金（2021J240）. 第一作者:王丽（1995－）,女,浙江宁波人,在读硕士研究生/医师,主要研究方向:心血管医学.E-mail:2011105041@nbu.edu.cn *通信作者:何文明（1981－）,男,浙江绍兴人,博士/主任医师,主要研究方向:心血管医学.E-mail:fyhewenming@nbu.edu.cn 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ |
