您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2023, Vol. 61 ›› Issue (12): 13-20.doi: 10.6040/j.issn.1671-7554.0.2023.0803

• 医学影像人工智能的创新与挑战—专家综述 • 上一篇    下一篇

影像人工智能在肺炎筛查、诊断及预测领域的应用研究进展

李骁,孙志远,张龙江*()   

  1. 南京大学医学院附属金陵医院(东部战区总医院)放射诊断科，江苏 南京 210002
  • 收稿日期:2023-08-31 出版日期:2023-12-10 发布日期:2024-01-11
  • 通讯作者: 张龙江 E-mail:kevinzhlj@163.com
  • 作者简介:张龙江，东部战区总医院放射诊断科主任，主任医师，教授，博士研究生导师，“长江学者奖励计划”特聘教授，国家优青，百千万人才工程国家级人选并被授予有突出贡献的中青年专家，国家重点研发计划首席科学家，联勤保障部队科技顶尖人才，江苏省百名医德之星。中华医学会放射学分会第十六届委员会委员兼心胸学组副组长、江苏省放射学会副主任委员、中国老年学学会放射学分会副会长。兼任《国际医学放射学杂志》副主编、《Journal Thoracic Imaging》血栓栓塞领域责任编辑等学术期刊编委
  • 基金资助:
    国家自然科学基金(82202150);国家自然科学基金(82371958)

Research advances of artificial intelligence-based medical imaging in the screening, diagnosis and prediction of pneumonia

Xiao LI,Zhiyuan SUN,Longjiang ZHANG*()   

  1. Department of Medical Imaging, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, Jiangsu, China
  • Received:2023-08-31 Online:2023-12-10 Published:2024-01-11
  • Contact: Longjiang ZHANG E-mail:kevinzhlj@163.com

RichHTML

28

PDF (PC)

1456

摘要：

肺炎是继缺血性心脏病和脑血管疾病之后的全球第三大死因，是严重威胁人类健康的重大公共卫生问题，早期快速精准的病因学诊断及危险性预测是肺炎诊疗和防控的首要任务，但影像科医生工作量大、不同类型肺炎影像表现重叠等原因使得肺炎及时、快速、准确诊断与结局预测有较大挑战。人工智能(AI)在影像领域的迅速发展为解决上述临床难题带来希望。本文对AI在肺炎诊断中的最新研究成果进行综述，旨在探讨AI系统在肺炎筛查、诊断、预测领域的最新进展，展望其应用前景，为促进我国合理优化肺炎患者临床管理，提升肺炎智能诊疗水平提供参考。

关键词: 影像, 人工智能, 肺炎, 筛查, 诊断, 预测

Abstract:

Pneumonia has become the third leading cause of death in the world after ischemic heart disease and cerebrovascular disease, and is a major public health problem that seriously threatens human health. Early, rapid and accurate etiological diagnosis and risk prediction are the primary tasks in the diagnosis, treatment and prevention of pneumonia. However, due to the heavy workload of radiologists and overlapping image manifestations of different types of pneumonia, timely, rapid, and accurate diagnosis and prediction is rather challenging. The rapid development of artificial intelligence (AI) in the imaging field offers hope for solving these clinical challenges. This paper reviews the latest research results of AI in the diagnosis of pneumonia, aiming to discuss the latest progress of AI system in the field of screening, diagnosis and prediction of pneumonia, and provide prospects in the field of pneumonia, so as to provide references for promoting reasonable optimization of clinical management of pneumonia patients in China and improving the level of intelligent diagnosis and treatment of pneumonia.

Key words: Radiology, Artificial intelligence, Pneumonia, Screening, Diagnosis, Prediction

中图分类号: 

  • R563.1
1 GBD Lower Respiratory Infections Collaborators , Troeger C , Forouzanfar M , Rao P , et al. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990-2016: a systematic analysis for the global burden of disease study 2016[J]. Lancet Infect Dis, 2018, 18 (11): 1191- 1210.
doi: 10.1016/S1473-3099(18)30310-4
2 Cilloniz C , Albert RK , Liapikou A , et al. The effect of macrolide resistance on the presentation and outcome of patients hospitalized for Streptococcus pneumoniae pneumonia[J]. Am J Respir Crit Care Med, 2015, 191 (11): 1265- 1272.
doi: 10.1164/rccm.201502-0212OC
3 Torres A , Blasi F , Peetermans WE , et al. The aetiology and antibiotic management of community-acquired pneumonia in adults in Europe: a literature review[J]. Eur J Clin Microbiol Infect Dis, 2014, 33 (7): 1065- 1079.
doi: 10.1007/s10096-014-2067-1
4 Cilloniz C , Ferrer M , Liapikou A , et al. Acute respiratory distress syndrome in mechanically ventilated patients with community-acquired pneumonia[J]. Eur Respir J, 2018, 51 (3): 1702215.
doi: 10.1183/13993003.02215-2017
5 Claessens YE , Debray MP , Tubach F , et al. Early chest computed tomography scan to assist diagnosis and guide treatment decision for suspected community-acquired pneumonia[J]. Am J Respir Crit Care Med, 2015, 192 (8): 974- 982.
doi: 10.1164/rccm.201501-0017OC
6 Ravizza S , Huschto T , Adamov A , et al. Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data[J]. Nat Med, 2019, 25 (1): 57- 59.
doi: 10.1038/s41591-018-0239-8
7 Esteva A , Robicquet A , Ramsundar B , et al. A guide to deep learning in healthcare[J]. Nat Med, 2019, 25 (1): 24- 29.
doi: 10.1038/s41591-018-0316-z
8 Topol EJ . High-performance medicine: the convergence of human and artificial intelligence[J]. Nat Med, 2019, 25 (1): 44- 56.
doi: 10.1038/s41591-018-0300-7
9 Ma JC , Song Y , Tian X , et al. Survey on deep learning for pulmonary medical imaging[J]. Front Med, 2020, 14 (4): 450- 469.
doi: 10.1007/s11684-019-0726-4
10 Chassagnon G , Vakalopolou M , Paragios N , et al. Deep learning: definition and perspectives for thoracic imaging[J]. Eur Radiol, 2020, 30 (4): 2021- 2030.
doi: 10.1007/s00330-019-06564-3
11 Mostafa FA , Elrefaei LA , Fouda MM , et al. A survey on AI techniques for thoracic diseases diagnosis using medical images[J]. Diagnostics, 2022, 12 (12): 3034.
doi: 10.3390/diagnostics12123034
12 Yamashita R , Nishio M , Do RKG , et al. Convolutional neural networks: an overview and application in radiology[J]. Insights Imaging, 2018, 9 (4): 611- 629.
doi: 10.1007/s13244-018-0639-9
13 Donahue J , Hendricks LA , Rohrbach M , et al. Long-term recurrent convolutional networks for visual recognition and description[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39 (4): 677- 691.
doi: 10.1109/TPAMI.2016.2599174
14 Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Advances in neural information processing systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014[C]. Montreal: Curran Associates, Inc., 2014.
15 Wang XS, Peng YF, Lu L, et al. ChestX-Ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common Thorax diseases[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017: 3462-3471. doi: 10.1109/CVPR.2017.369.
16 Bustos A , Pertusa A , Salinas JM , et al. PadChest: a large chest X-ray image dataset with multi-label annotated reports[J]. Med Image Anal, 2020, 66, 101797.
doi: 10.1016/j.media.2020.101797
17 Wang LD , Lin ZQ , Wong A . COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images[J]. Sci Rep, 2020, 10 (1): 19549.
doi: 10.1038/s41598-020-76550-z
18 Rahman T , Khandakar A , Qiblawey Y , et al. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images[J]. Comput Biol Med, 2021, 132, 104319.
doi: 10.1016/j.compbiomed.2021.104319
19 Johnson AEW , Pollard TJ , Berkowitz SJ , et al. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports[J]. Sci Data, 2019, 6 (1): 317.
doi: 10.1038/s41597-019-0322-0
20 Kermany DS , Goldbaum M , Cai WJ , et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172 (5): 1122- 1131.
doi: 10.1016/j.cell.2018.02.010
21 Dong X , Lei Y , Wang TH , et al. Automatic multiorgan segmentation in thorax CT images using U-net-GAN[J]. Med Phys, 2019, 46 (5): 2157- 2168.
doi: 10.1002/mp.13458
22 Park J , Yun J , Kim N , et al. Fully automated lung lobe segmentation in volumetric chest CT with 3D U-net: validation with intra- and extra-datasets[J]. J Digit Imaging, 2020, 33 (1): 221- 230.
doi: 10.1007/s10278-019-00223-1
23 Yun J , Park J , Yu D , et al. Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net[J]. Med Image Anal, 2019, 51, 13- 20.
doi: 10.1016/j.media.2018.10.006
24 Allioui H , Mohammed MA , Benameur N , et al. A multi-agent deep reinforcement learning approach for enhancement of COVID-19 CT image segmentation[J]. J Pers Med, 2022, 12 (2): 309.
doi: 10.3390/jpm12020309
25 Dey N , Zhang YD , Rajinikanth V , et al. Customized VGG19 architecture for pneumonia detection in chest X-rays[J]. Pattern Recognit Lett, 2021, 143, 67- 74.
doi: 10.1016/j.patrec.2020.12.010
26 El Asnaoui K, Chawki Y, Idri A. Automated methods for detection and classification pneumonia based on X-ray images using deep learning[M]// Artificial Intelligence and Blockchain for Future Cybersecurity Applications. Cham: Springer, 2021: 257-284.10.1007/978-3-030-74575-2_14.
27 Wang S , Kang B , Ma JL , et al. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)[J]. Eur Radiol, 2021, 31 (8): 6096- 6104.
doi: 10.1007/s00330-021-07715-1
28 McCall B . COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread[J]. Lancet Digit Health, 2020, 2 (4): e166- e167.
doi: 10.1016/S2589-7500(20)30054-6
29 Ni QQ , Sun ZY , Qi L , et al. A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images[J]. Eur Radiol, 2020, 30 (12): 6517- 6527.
doi: 10.1007/s00330-020-07044-9
30 Chen X , Zhang G , Hao SY , et al. Similarities and differences of early pulmonary CT features of pneumonia caused by SARS-CoV-2, SARS-CoV and MERS-CoV: comparison based on a systemic review[J]. Chin Med Sci J, 2020, 35 (3): 254- 261.
doi: 10.24920/003727
31 Fowlkes A , Steffens A , Temte J , et al. Incidence of medically attended influenza during pandemic and post-pandemic seasons through the Influenza Incidence Surveillance Project, 2009-13[J]. Lancet Respir Med, 2015, 3 (9): 709- 718.
doi: 10.1016/S2213-2600(15)00278-7
32 Bai HX , Wang R , Xiong Z , et al. Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT[J]. Radiology, 2020, 296 (3): E156- E165.
doi: 10.1148/radiol.2020201491
33 Wang YL , Gao D , Geng ZJ , et al. Radiomics nomogram analyses for differentiating pneumonia and acute paraquat lung injury[J]. Sci Rep, 2019, 9 (1): 15029.
doi: 10.1038/s41598-019-50886-7
34 Gao L , Li YZ , Zhai ZG , et al. Radiomics study on pulmonary infarction mimicking community-acquired pneumonia[J]. Clin Respir J, 2021, 15 (6): 661- 669.
doi: 10.1111/crj.13341
35 Mamalakis M , Swift AJ , Vorselaars B , et al. DenResCov-19: a deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays[J]. Comput Med Imaging Graph, 2021, 94, 102008.
doi: 10.1016/j.compmedimag.2021.102008
36 Huang YL , Zhang ZG , Liu SY , et al. CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia[J]. BMC Med Imaging, 2021, 21 (1): 31.
doi: 10.1186/s12880-021-00564-w
37 Zhang K , Liu X , Shen J , et al. Clinically applicable AI System for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography[J]. Cell, 2020, 181 (6): 1423- 1433.
doi: 10.1016/j.cell.2020.04.045
38 Chen W , Xiong X , Xie B , et al. Pulmonary invasive fungal disease and bacterial pneumonia: a comparative study with high-resolution CT[J]. Am J Transl Res, 2019, 11 (7): 4542- 4551.
39 Li X , Fang X , Bian Y , et al. Comparison of chest CT findings between COVID-19 pneumonia and other types of viral pneumonia: a two-center retrospective study[J]. Eur Radiol, 2020, 30 (10): 5470- 5478.
doi: 10.1007/s00330-020-06925-3
40 Fang X , Li X , Bian Y , et al. Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2[J]. Eur Radiol, 2020, 30 (12): 6888- 6901.
doi: 10.1007/s00330-020-07032-z
41 Kim J , Kim J , Kim G , et al. Clinical validation of a deep learning algorithm for detection of pneumonia on chest radiographs in emergency department patients with acute febrile respiratory illness[J]. J Clin Med, 2020, 9 (6): 1981.
doi: 10.3390/jcm9061981
42 Wang G , Liu X , Shen J , et al. A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images[J]. Nat Biomed Eng, 2021, 5 (6): 509- 521.
doi: 10.1038/s41551-021-00704-1
43 Matsuo K , Purushotham S , Jiang B , et al. Survival outcome prediction in cervical cancer: Cox models vs deep-learning model[J]. Am J Obstet Gynecol, 2019, 220 (4): 381.
doi: 10.1016/j.ajog.2018.12.030
44 程召平, 段艳华, 姚金坤, 等. 105例新型冠状病毒感染胸部CT影像学特征—山东省多中心回顾性分析[J]. 山东大学学报(医学版), 2020, 58 (5): 38- 45.
CHENG Zhaoping , DUAN Yanhua , YAO Jinkun , et al. Chest CT features of 105 patients with COVID-19: a multicenter retrospective study in Shandong Province[J]. Journal of Shandong University (Health Sciences), 2020, 58 (5): 38- 45.
45 Aboutalebi H , Pavlova M , Shafiee MJ , et al. COVID-net CXR-S: deep convolutional neural network for severity assessment of COVID-19 cases from chest X-ray images[J]. Diagnostics, 2021, 12 (1): 25.
doi: 10.3390/diagnostics12010025
46 Liang W , Yao J , Chen A , et al. Early triage of critically ill COVID-19 patients using deep learning[J]. Nat Commun, 2020, 11 (1): 3543- 3549.
doi: 10.1038/s41467-020-17280-8
47 Xu Q , Zhan X , Zhou Z , et al. AI-based analysis of CT images for rapid triage of COVID-19 patients[J]. NPJ Digit Med, 2021, 4 (1): 75.
doi: 10.1038/s41746-021-00446-z
48 Marboub D , Abbasi T , Maasmi F , et al. Blockchain for COVID-19: review, opportunities, and a trusted tracking system[J]. Arab J Sci Eng, 2020, 45 (12): 9895- 9911.
doi: 10.1007/s13369-020-04950-4
49 Kim D , Jang H , Kim K , et al. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers[J]. Korean J Radiol, 2019, 20 (3): 405- 410.
doi: 10.3348/kjr.2019.0025
50 INFANT Collaborative Group . Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial[J]. Lancet, 2017, 389 (10080): 1719- 1729.
doi: 10.1016/S0140-6736(17)30568-8
[1] 李波波 李道堂 刘曙光 王兴武. 食管癌患者血清中DKK-1的表达[J]. 山东大学学报(医学版), 2209, 47(6): 58-61.
[2] 徐平 于国放 李霞. 不同类型甲状腺上动脉PSV对Graves病与桥本氏甲状腺炎鉴别诊断的价值[J]. 山东大学学报(医学版), 2209, 47(6): 62-64.
[3] 王欣，邢春燕，杨艳平. 血清磷酸丙酮酸水合酶检测对诊断侵袭性白念珠菌感染的临床价值[J]. 山东大学学报(医学版), 2209, 47(6): 92-94.
[4] 梁晨,姚丽娟,吕龙飞,唐泽,秦达,崔有斌,余孝淇. 三维重建联合CT引导下穿刺定位在微创治疗肺磨玻璃结节中的应用[J]. 山东大学学报 (医学版), 2026, 64(5): 74-82.
[5] 山东省医学会心电学分会山东省医学伦理学学会心电学分会. 心电图报告规范化书写专家共识(2026)[J]. 山东大学学报 (医学版), 2026, 64(4): 1-7.
[6] 王烨,王刚,南海鸥,蔡雨玲,李嘉宇,王海锋,颜英杰,江志伟. 胃癌患者围手术期心率变异度与炎性指标的相关性[J]. 山东大学学报 (医学版), 2026, 64(4): 23-30.
[7] 段盈竹,董波,于睿. 内在情感与类风湿关节炎患者冠状动脉粥样硬化风险关系的孟德尔随机化分析[J]. 山东大学学报 (医学版), 2026, 64(4): 63-71.
[8] 中国医师协会骨科医师分会智能骨科学组,中华预防医学会脊柱疾病预防与控制专业委员会脊柱脊髓损伤疾病预防与控制学组. 人工智能脊柱退变影像学测量位点与标注专家共识(2025)[J]. 山东大学学报 (医学版), 2026, 64(2): 1-10.
[9] 季心宇,余思沂,孙圆圆,姬冰. 基于人工智能和步态分析的骨科疾病辅助诊疗方法[J]. 山东大学学报 (医学版), 2026, 64(2): 34-43.
[10] 王建民,李晓峰,由志涛,董圣杰,赵宇驰,李占菊,邹德鑫,张剑锋,孙涛,杜伟. 基于可解释机器学习的后路腰椎椎体间融合术后慢性疼痛风险预测模型构建[J]. 山东大学学报 (医学版), 2026, 64(2): 78-88.
[11] 孙爽爽,仉率杰,张伯韬,袁莹,于媛媛,薛付忠. 基于真实世界研究的18~50岁人群急性缺血性卒中影响因素[J]. 山东大学学报 (医学版), 2025, 63(9): 40-46.
[12] 王梦星,薛付忠,杨帆. 基于多模态交叉注意力机制融合的1型糖尿病血糖浓度预测方法[J]. 山东大学学报 (医学版), 2025, 63(8): 41-50.
[13] 申路佳,逯天威,巩伟明,赵岩松,王淑康,袁中尚. 代谢风险评分在2型糖尿病人群心血管结局预测中的应用[J]. 山东大学学报 (医学版), 2025, 63(8): 69-78.
[14] 李千,杨帆,薛付忠. 基于多模态数据融合的多癌种风险预测模型[J]. 山东大学学报 (医学版), 2025, 63(8): 79-85.
[15] 李晓琪,刘佩丽,成红,赵艳艳. 基于自注意力机制预测ICU脓毒症患者的死亡率[J]. 山东大学学报 (医学版), 2025, 63(8): 86-93.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发