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山东大学学报 (医学版) ›› 2023, Vol. 61 ›› Issue (12): 7-12, 20.doi: 10.6040/j.issn.1671-7554.0.2023.0705

• 医学影像人工智能的创新与挑战—专家综述 • 上一篇    下一篇

影像人工智能在医学领域的时代创新与挑战

徐子良,郑敏文*()   

  1. 中国人民解放军空军军医大学第一附属医院放射科,陕西 西安 710032
  • 收稿日期:2023-08-10 出版日期:2023-12-10 发布日期:2024-01-11
  • 通讯作者: 郑敏文 E-mail:zhengmw2007@163.com
  • 作者简介:郑敏文,中国人民解放军空军军医大学第一附属医院(西京医院)放射科主任,医学影像学教研室主任,博士研究生导师,医学博士,主任医师、教授。国内著名心血管影像专家,长期致力于心血管影像的临床及科研工作。学术任职:中华医学会放射学分会常委兼心胸学组组长、中国医师协会放射医师分会常委兼急诊工作组组长、陕西省医师协会放射医师分会会长、陕西省医学会放射学分会副主委。奖项荣誉:先后获陕西省科学技术二等奖2项、陕西省教学成果一等奖1项、校级教学成果二等奖1项。研究成果:承担科技部重点研发计划项目子课题1项、国家卫健委数据库项目1项、国家自然科学基金4项、省部级课题5项。以第一或通讯作者发表SCI论文40余篇,主编国家级教材专著3部、副主编著作4部,参编教育部高校规划教材3部,牵头中国专家共识5部,参与制定行业标准2部、中国指南/专家共识10部
  • 基金资助:
    国家重点研发计划(2022YFA1004204)

Innovation and challenge of imaging artificial intelligence in medical field

Ziliang XU,Minwen ZHENG*()   

  1. Department of Radiology, The First Affiliated Hospital, Air Force Medical University of PLA, Xi'an 710032, Shaanxi, China
  • Received:2023-08-10 Online:2023-12-10 Published:2024-01-11
  • Contact: Minwen ZHENG E-mail:zhengmw2007@163.com

摘要:

随着科技的发展,人工智能(AI)技术正逐渐应用于医学影像领域,但是AI技术仍面临诸多挑战。论文将分别从组织分割、疾病辅助诊断及临床研究三个方面综述影像AI技术在医学领域的应用进展,同时指出目前AI技术应用存在的问题。最后针对影像AI技术在医学领域中面临的挑战进行述评。

关键词: 医学影像, 人工智能, 深度学习, 计算机辅助诊断

Abstract:

With the development of science and technology, artificial intelligence (AI) has been applied in the medical imaging field gradually. However, the AI still faces many challenges. In this paper, the imaging application progress of AI in medical field will be reviewed from the aspect of tissue segmentation, auxiliary diagnosis of disease and clinical research, respectively, and the problems in them will also be pointed out. Finally, the challenges of imaging AI in medical field will be discussed.

Key words: Medical imaging, Artificial intelligence, Deep learning, Computer-aided diagnosis

中图分类号: 

  • R816
1 Russell SJ , Norvig P . Artificial Intelligence, A Modern Approach - 2nd Edition[M]. Upper Saddle River, New Jersey: Prentice Hall, 2003.
2 Chen X , Wang X , Zhang K , et al. Recent advances and clinical applications of deep learning in medical image analysis[J]. Med Image Anal, 2022, 79, 102444.
doi: 10.1016/j.media.2022.102444
3 Shokraei Fard A , Reutens DC , Vegh V . From CNNs to GANs for cross-modality medical image estimation[J]. Comput Biol Med, 2022, 146, 105556.
doi: 10.1016/j.compbiomed.2022.105556
4 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.
doi: 10.1161/circimaging.119.009214
5 Aggarwal R , Sounderajah V , Martin G , et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis[J]. NPJ Digit Med, 2021, 4 (1): 65.
doi: 10.1038/s41746-021-00438-z
6 Johnson KW , Torres Soto J , Glicksberg BS , et al. Artificial intelligence in cardiology[J]. J Am Coll Cardiol, 2018, 71 (23): 2668- 2679.
doi: 10.1016/j.jacc.2018.03.521
7 Jain S , Indora S , Atal DK . Lung nodule segmentation using Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network[J]. Comput Biol Med, 2021, 137, 104811.
doi: 10.1016/j.compbiomed.2021.104811
8 Zhao X , Wu Y , Song G , et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation[J]. Med Image Anal, 2018, 43, 98- 111.
doi: 10.1016/j.media.2017.10.002
9 Alizadehsani R , Hosseini MJ , Khosravi A , et al. Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries[J]. Comput Methods Programs Biomed, 2018, 162, 119- 127.
doi: 10.1016/j.cmpb.2018.05.009
10 Rajendra Acharya U , Meiburger KM , Wei Koh JE , et al. Automated plaque classification using computed tomography angiography and Gabor transformations[J]. Artif Intell Med, 2019, 100, 101724.
doi: 10.1016/j.artmed.2019.101724
11 Han D , Kolli KK , Gransar H , et al. Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches[J]. J Cardiovasc Comput Tomogr, 2020, 14 (2): 168- 176.
doi: 10.1016/j.jcct.2019.09.005
12 Han D , Kolli KK , Al'Aref SJ , et al. Machine learning framework to identify individuals at risk of rapid progression of coronary atherosclerosis: From the PARADIGM registry[J]. J Am Heart Assoc, 2020, 9 (5): e013958.
doi: 10.1161/jaha.119.013958
13 Pham DL , Xu C , Prince JL . Current methods in medical image segmentation[J]. Annu Rev Biomed Eng, 2000, 2, 315- 337.
doi: 10.1146/annurev.bioeng.2.1.315
14 Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation[C]. MICCAI 2015, 2015: 234-241. doi: 10.1007/978-3-319-24574-4_28.
15 Shaukat Z , Farooq QUA , Tu S , et al. A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture[J]. BMC Bioinformatics, 2022, 23 (1): 251.
doi: 10.1186/s12859-022-04794-9
16 Li D , Chu X , Cui Y , et al. Improved U-Net based on contour prediction for efficient segmentation of rectal cancer[J]. Comput Methods Programs Biomed, 2022, 213, 106493.
doi: 10.1016/j.cmpb.2021.106493
17 Li C , Song X , Zhao H , et al. An 8-layer residual U-Net with deep supervision for segmentation of the left ventricle in cardiac CT angiography[J]. Comput Methods Programs Biomed, 2021, 200, 105876.
doi: 10.1016/j.cmpb.2020.105876
18 Lian L , Luo X , Pan C , et al. Lung image segmentation based on DRD U-Net and combined WGAN with Deep Neural Network[J]. Comput Methods Programs Biomed, 2022, 226, 107097.
doi: 10.1016/j.cmpb.2022.107097
19 Jiang L , Ou J , Liu R , et al. RMAU-Net: Residual multi-scale attention U-Net for liver and tumor segmentation in CT images[J]. Comput Biol Med, 2023, 158, 106838.
doi: 10.1016/j.compbiomed.2023.106838
20 Zhou Z , Siddiquee MMR , Tajbakhsh N , et al. UNet++: a nested U-Net architecture for medical image segmentation[J]. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018), 2018, 11045, 3- 11.
doi: 10.1007/978-3-030-00889-5_1
21 Xu Y , Hou S , Wang X , et al. A medical image segmentation method based on improved UNet 3+ network[J]. Diagnostics (Basel), 2023, 13 (3): 576.
doi: 10.3390/diagnostics13030576
22 Isensee F , Jaeger PF , Kohl SAA , et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nat Methods, 2021, 18 (2): 203- 211.
doi: 10.1038/s41592-020-01008-z
23 Malhotra P , Gupta S , Koundal D , et al. Deep neural networks for medical image segmentation[J]. J Healthc Eng, 2022, 2022, 9580991.
doi: 10.1155/2022/9580991
24 van Ginneken B , Schaefer-Prokop CM , Prokop M . Computer-aided diagnosis: how to move from the laboratory to the clinic[J]. Radiology, 2011, 261 (3): 719- 732.
doi: 10.1148/radiol.11091710
25 Khav N , Ihdayhid AR , Ko B . CT-Derived Fractional Flow Reserve (CT-FFR) in the evaluation of coronary artery disease[J]. Heart Lung Circ, 2020, 29 (11): 1621- 1632.
doi: 10.1016/j.hlc.2020.05.099
26 Balasubramanian PK , Lai WC , Seng GH , et al. APESTNet with mask R-CNN for liver tumor segmentation and classification[J]. Cancers (Basel), 2023, 15 (2): 33.
27 Wang X , Li BB . Deep learning in head and neck tumor multiomics diagnosis and analysis: review of the literature[J]. Front Genet, 2021, 12, 624820.
doi: 10.3389/fgene.2021.624820
28 Turhan G , Küçük H , Isik EO . Spatio-temporal convolution for classification of alzheimer disease and mild cognitive impairment[J]. Comput Methods Programs Biomed, 2022, 221, 106825.
doi: 10.1016/j.cmpb.2022.106825
29 Sahin ME , Ulutas H , Yuce E , et al. Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images[J]. Neural Comput Appl, 2023, 35 (18): 13597- 13611.
doi: 10.1007/s00521-023-08450-y
30 Eisenberg E , McElhinney PA , Commandeur F , et al. Deep learning-based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects[J]. Circ Cardiovasc Imaging, 2020, 13 (2): e009829.
doi: 10.1161/circimaging.119.009829
31 van der Voort SR , Incekara F , Wijnenga MMJ , et al. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning[J]. Neuro Oncol, 2023, 25 (2): 279- 289.
doi: 10.1093/neuonc/noac166
32 Ouyang D , He B , Ghorbani A , et al. Video-based AI for beat-to-beat assessment of cardiac function[J]. Nature, 2020, 580 (7802): 252- 256.
doi: 10.1038/s41586-020-2145-8
33 Ruijsink B , Puyol-Antón E , Oksuz I , et al. Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function[J]. JACC Cardiovasc Imaging, 2020, 13 (3): 684- 695.
doi: 10.1016/j.jcmg.2019.05.030
34 Knott KD , Seraphim A , Augusto JB , et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping[J]. Circulation, 2020, 141 (16): 1282- 1291.
35 Aerts HJ , Velazquez ER , Leijenaar RT , et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nat Commun, 2014, 5, 4006.
doi: 10.1038/ncomms5006
36 Baessler B , Luecke C , Lurz J , et al. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure[J]. Radiology, 2019, 292 (3): 608- 617.
doi: 10.1148/radiol.2019190101
37 Di Noto T , von Spiczak J , Mannil M , et al. Radiomics for distinguishing myocardial infarction from myocarditis at late gadolinium enhancement at MRI: comparison with subjective visual analysis[J]. Radiol Cardiothorac Imaging, 2019, 1 (5): e180026.
doi: 10.1148/ryct.2019180026
38 Sun Q , Chen Y , Liang C , et al. Biologic pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma[J]. Radiology, 2021, 301 (3): 654- 663.
doi: 10.1148/radiol.2021203281
39 Kickingereder P , Neuberger U , Bonekamp D , et al. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma[J]. Neuro Oncol, 2018, 20 (6): 848- 857.
doi: 10.1093/neuonc/nox188
40 Xi YB , Guo F , Xu ZL , et al. Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma[J]. J Magn Reson Imaging, 2018, 47 (5): 1380- 1387.
doi: 10.1002/jmri.25860
41 Wang X , Xie T , Luo J , et al. Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment[J]. Breast Cancer Res, 2022, 24 (1): 20.
doi: 10.1186/s13058-022-01516-0
42 Honeycutt CE , Plotnick R . Image analysis techniques and gray-level co-occurrence matrices (GLCM) for calculating bioturbation indices and characterizing biogenic sedimentary structures[J]. Computers & Geosciences, 2008, 34 (11): 1461- 1472.
43 徐子良, 郑敏文. 人工智能在心血管影像的应用现状与展望[J]. 中华放射学杂志, 2021, 55 (6): 5.
44 Fukushima K , Miyake S . Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition, Competition and cooperation in neural nets[M]. Berlin, Heidelberg: Springer, 1982.
45 van der Velden BHM , Kuijf HJ , Gilhuijs KGA , et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis[J]. Med Image Anal, 2022, 79, 102470.
doi: 10.1016/j.media.2022.102470
46 Diprose WK , Buist N , Hua N , et al. Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator[J]. J Am Med Inform Assoc, 2020, 27 (4): 592- 600.
doi: 10.1093/jamia/ocz229
47 Alkhodari M , Jelinek HF , Karlas A H , et al. Deep learning predicts heart failure with preserved, mid-range, and reduced left ventricular ejection fraction from patient clinical profiles[J]. Front Cardiovasc Med, 2021, 8, 755968.
doi: 10.3389/fcvm.2021.755968
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