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:: Volume 22, Issue 1 (1-2024) ::
Int J Radiat Res 2024, 22(1): 199-205 Back to browse issues page
Diagnostic value of chest computed tomography scan based on artificial intelligence and deep learning in children with lobar pneumonia and analysis of image features before and after treatment: A retrospective cohort study
L. Chen , S. Dong , Y. Chen , L. Tian , C. He , S. Tao
Department of Paediatrics, Wuhan Fourth Hospital, Wuhan City, Hubei Province, China. 430033 , xychenyongli@163.com
Abstract:   (230 Views)
Background: A retrospective cohort study was conducted to analyze the diagnostic value and image features of chest computed tomography (CT) scan in children with lobar pneumonia (LP) before and after treatment. Materials and Methods: 172 children with lobar pneumonia treated from January 2016 to December 2021 were selected. The patients who underwent plain X-ray scan were divided into control group (n = 72) and the patients who underwent chest CT scan as study group (n = 100). The diagnostic value and image characteristics before and after treatment were compared between the two groups. Results: After treatment, the lesion area of the patient was absorbed in varying degrees, and the CT plain scan indicated that the solid shadow density decreased until it was completely absorbed. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of chest X-ray were 66.67%, 58.33%, 63.89%, 76.19% and 46.67% respectively; and chest CT scan were 82.98%, 67.92%, 75.00%, 69.64% and 81.82%. The sensitivity, specificity, accuracy, and negative predictive value of chest CT plain scan were higher, and the positive predictive value was lower compared to those of chest X-ray plain film. The results of ROC curve study indicated that the AUC of chest CT plain scan was 0.755 (95%CI=0.657-0.852), and the AUC of chest X-ray film was 0.625 (95%CI= 0.489-0.744). Conclusion: Chest CT has high sensitivity and specificity in the diagnosis of LP in children, which can clearly demonstrate the imaging features of LP before and after treatment.
Keywords: Artificial intelligence, deep learning, computed tomography, lobar pneumonia, image analysis.
Full-Text [PDF 707 kb]   (112 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. Zinserling VA, Swistunov VV, Botvinkin AD, et al. (2022) Lobar (croupous) pneumonia: old and new data. Infection, 50(1): 235-242. [DOI:10.1007/s15010-021-01689-4]
2. Deng LN, Zhang GJ, Lin XQ (2021) Comparative study of energy spectrum and perfusion CT imaging in differential diagnosis of peripheral lung cancer and focal organized pneumonia [J/OL]. Chinese Journal of Medical Imaging, 12: (09) 1-6.
3. Haghbayan M, Khatami S S, Nasrollahi Heravi F (2021) The estimation of newly infected cases of covid-19 with consideration of governmental action and behavior of people in Iran. SJMSHM, 3(1): 1-7.
4. Yu YX, Li M, Gu L (2021) Study on early recognition of severe COVID-19 by nomogram based on clinical and CT features. Journal of Clinical Radiology, 40: (06) 1106-1111.
5. Rujittika M and Viroj W (2021) Letter to the Editor: Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using DL technology and blood test results. Japanese Journal of Radiology, 39: (10) 176-179. [DOI:10.1007/s11604-021-01179-5]
6. Chen WT, Sun L, Tan AB (2021) Prediction of radiation pneumonitis in radiotherapy for lung cancer based on CT imaging characteristics and clinical physical dose characteristics. Chinese Journal of Medical Physics, 38: (06) 672-676.
7. Wei J, Zhu RH, Zhang H (2021) Application of PET/CT image under convolutional neural network model in postoperative pneumonia virus infection monitoring of patients with non-small cell lung cancer. Results in Physics, 26: (56) 34-38. [DOI:10.1016/j.rinp.2021.104385]
8. Shuang W, Yi Z, Liu YK (2021) Analysis of image features and TCM syndrome types of lobar pneumonia in children based on mean square deviation lung CT image registration algorithm. Scientific Programming, 8: 2021. [DOI:10.1155/2021/4016914]
9. Mihaela RL (2021) DL in classification of covid-19 coronavirus, pneumonia and healthy lungs on CXR and CT images. Journal of Medical and Biological Engineering, 5: (67) 166-169.
10. Zhang MD, Yu SW, Yin XT (2021) An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images. Japanese journal of radiology, 39: (10) 14-17. [DOI:10.1007/s11604-021-01136-2]
11. Argentieri GL, Bellesi L, Pagnamenta A (2021) Diagnostic yield, safety, and advantages of ultra-low dose chest CT compared to chest radiography in early stage suspected SARS-CoV-2 pneumonia: A retrospective observational study. Medicine, 100: (21) 566-569. [DOI:10.1097/MD.0000000000026034]
12. Wang N, Hou ZB, Wang CX (2021) Diagnostic value of CTA in children with severe mycoplasma pneumonia complicated with vascular embolism. Radiology Practice, 36: (05) 648-652.
13. Konietzke P, Steentoft HaukH, Wagner WL (2021) Consolidated lung on contrast-enhanced chest CT: the use of spectral-detector computed tomography parameters in differentiating atelectasis and pneumonia. Heliyon, 7: (5) 45-49. [DOI:10.1016/j.heliyon.2021.e07066]
14. Jeroen CJ, Richard AT, Frans K, et al. (2020) Signs of pulmonary infection on admission chest computed tomography are associated with pneumonia or death in patients with acute stroke. Stroke, 51:1690-1695. [DOI:10.1161/STROKEAHA.120.028972]
15. Romanov A, Bach M, Yang S (2021) Automated CT lung density analysis of viral pneumonia and healthy lungs using DL-based segmentation, histograms and HU thresholds. Diagnostics (Basel, Switzerland), 11: (5) 54-58. [DOI:10.3390/diagnostics11050738]
16. Ma MT, Xie Q, Wang WL (2021) Comparison of the value of chest plain film, pulmonary ultrasound and CT in the diagnosis of pneumonia in children . Chinese CT and MRI Magazine, 19: (05) 30-32.
17. Yazaki K, Nonaka M, Shigemasa R (2021) Usual interstitial pneumonia progressing to nonspecific interstitial pneumonia-like pattern on high-resolution CT with histologic confirmation. Radiology Case Reports, 16: (5) 14-19. [DOI:10.1016/j.radcr.2021.02.019]
18. Lu S, Xing ZH, Zhao SY (2021) Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method. International Journal of Endocrinology, 21: (62) 34-39. [DOI:10.1155/2021/6616069]
19. Das KM, Alkoteesh JA, Al KJ (2021) Comparison of chest radiography and chest CT for evaluation of pediatric COVID-19 pneumonia: Does CT add diagnostic value? Pediatric Pulmonology, 56: (6) 43-46. [DOI:10.1002/ppul.25313]
20. Auspicious L, Lei ZX, Wu SY (2021) Clinical and hematological characteristics of bronchopneumonia and LP in children with mycoplasma pneumoniae infection. Chinese Journal of Hospital Epidemiology, 31: (02) 281-285.
21. Wang J, Xia C, Sharma A (2021) Chest CT Findings and Differential Diagnosis of Mycoplasma pneumoniae Pneumonia and Mycoplasma pneumoniae Combined with Streptococcal Pneumonia in Children. Journal of Healthcare Engineering, 201: (5) 18-21. [DOI:10.1155/2021/8085530]
22. Savelli G, Bonacina M, Rizzo A (2020) Activated macrophages are the main inflammatory cell in COVID-19 interstitial pneumonia infiltrates. Is it possible to show their metabolic activity and thus the grade of inflammatory burden with 18 F-Fluorocholine PET/CT? Medical Hypotheses, 144: (6) 856-859. [DOI:10.1016/j.mehy.2020.109885]
23. Capristo C and Rossi GA (2017) Post-infectious persistent cough: pathogenesis and therapeutic options. Minerva Pediatr, 69: (5) 444-452. [DOI:10.23736/S0026-4946.17.04958-1]
24. Martini K, Blüthgen C, Walter JE (2020) Accuracy of Conventional and Machine Learning Enhanced Chest Radiography for the Assessment of COVID-19 Pneumonia: Intra-Individual Comparison with CT. Journal of Clinical Medicine, 9: (11) 784-789. [DOI:10.3390/jcm9113576]
25. Li WQ, Chen Y, Fu AS (2020) Serum procalcitonin, smoking history combined age established a new prediction model for predicting dynamic changes of chest CT images in adult community-acquired pneumonia (CAP) Patients. Clinical laboratory, 66: (11) 86-89. [DOI:10.7754/Clin.Lab.2020.200208]
26. Lung D and Conditions VP (2020) New viral pneumonia study results reported from respiratory intensive care unit (Atomatic Detection and Diagnosis of Severe Viral Pneumonia Ct Images Based On Lda-svm). Journal of Robotics & Machine Learning, 64: (4) 112-115.
27. Coronavirus - COVID-19 (2021) Findings from Shahid Beheshti University of Medical Sciences Provides New Data on COVID-19 (Risk Factors for Poor Outcome in Patients with Severe Viral Pneumonia on Chest CT during the COVID-19 Outbreak: a Perspective from Iran). Medical Letter on the CDC & FDA, 46: (3) 156-158.
28. Wang Y, Wu B, Zhang N, et al. (2020) Research progress of computer aided diagnosis system for pulmonary nodules in CT images. J Xray Sci Technol, 28: (1) 1-16. [DOI:10.3233/XST-190581]
29. AI (2020) New AI Study Findings Have Been Reported from Shenzhen University (Using AI To Detect Covid-19 and Community-acquired Pneumonia Based On Pulmonary Ct: Evaluation of the Diagnostic Accuracy). Medical Letter on the CDC & FDA, 30: (13) 145-148.
30. Tianyu X, Zhang WT, Qian BY (2020) The role and challenge of AI in new coronavirus pneumonia CT diagnosis. TMR Modern Herbal Medicine, 30: (3) 156-158. [DOI:10.53388/TMRmhm202003081]
31. Moritz S, Katharina M, Stephan S (2020) Pneumonia detection in chest X-ray dose-equivalent CT: impact of dose reduction on detectability by AI. Academic Radiology, 28: (67) 144-148.
32. Yue HM, Qian Y, Liu C (2020) Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study. Annals of Translational Medicine, 8: (14) 45-48. [DOI:10.21037/atm-20-3026]
33. Gao Y and Ren HF (2019) Analysis of the value of 64-slice CT thin-layer reconstruction and HRCT in the diagnosis of MP pneumonia in children. Chinese CT and MRI Magazine, 17: (12) 56-58+163.
34. Xu HF, Yang Y, Zhang XR (2017) CT findings of mycoplasma pneumoniae lung abscess in children (analysis of 12 cases). Radiology Practice, 32: (10) 1057-1059.
35. Zhang Y, Shang W, Song XM (2017) Differential diagnosis of Mycoplasma pneumoniae pneumonia and Mycoplasma pneumoniae complicated with Streptococcus pneumoniae pneumonia by chest CT in Children. Chinese Journal of Hospital Epidemiology, 27: (06) 1391-1393+1397.
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Chen L, Dong S, Chen Y, Tian L, He C, Tao S. Diagnostic value of chest computed tomography scan based on artificial intelligence and deep learning in children with lobar pneumonia and analysis of image features before and after treatment: A retrospective cohort study. Int J Radiat Res 2024; 22 (1) :199-205
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Volume 22, Issue 1 (1-2024) Back to browse issues page
International Journal of Radiation Research
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