Journal of Shandong University (Health Sciences) ›› 2023, Vol. 61 ›› Issue (12): 13-20.doi: 10.6040/j.issn.1671-7554.0.2023.0803

• The innovation and challenge of artificial intelligence in medical imaging—Expert Overview • Previous Articles     Next Articles

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

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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

CLC Number: 

  • 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
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