Application of CT images based on the optimal atlas segmentation algorithm in the clinical diagnosis of Mycoplasma Pneumoniae Pneumonia in Children

Abstract

Objective: Use of optimal Atlas segmentation algorithm to study the imaging signs of mycoplasma pneumonia with multi-slice spiral CT (HRCT), and to explore the value of HRCT in the diagnosis and efficacy in evaluation of mycoplasma pneumonia in children.
 Methods: The study retrospectively analyzed 72 patients diagnosed with mycoplasma pneumonia in our hospital from January 2017 to January 2019. The imaging data and clinical data of 72 patients were collected. The optimal Atlas segmentation algorithm was used to analyze the characteristics of CT examination, and the value of CT in the diagnosis of mycoplasma pneumonia and the evaluation of curative effect was summarized.
 Results: Among all patients, 37 cases were unilateral lesions, 35 cases were bilateral lesions, 19 cases were in the left upper lobe, 24 cases were in the left lower lobe, 21 cases were in the right upper lobe, 13 cases were in the right middle lobe, 25 The lesion was located in the right lower lobe. The main CT findings of the lesions before treatment were large patchy, spot-shaped shadows, and strip-shaped or ground-glass shadows. After treatment, the main CT findings of the lesions were reduced lesion density and reduced lesion range.
 Conclusion: CT can clearly show the pulmonary lesions of mycoplasma pneumonia, and its unique imaging signs can improve the clinical diagnosis accuracy. In addition, CT scans can evaluate the treatment effect according to the changes in the characteristics of the lesion, which has important value for the evaluation of the effect for clinical diagnosis and efficacy evaluation of mycoplasma pneumonia.
 doi: https://doi.org/10.12669/pjms.37.6-WIT.4860
 How to cite this:Fu X, Yang N, Ji J. Application of CT images based on the optimal atlas segmentation algorithm in the clinical diagnosis of Mycoplasma Pneumoniae Pneumonia in Children. Pak J Med Sci. 2021;37(6):1647-1651. doi: https://doi.org/10.12669/pjms.37.6-WIT.4860
 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Model based on the automated AI-Driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

“…Large pulmonary lesions can be seen on radiographs or CT scans in both the general and RMPP groups. The pathological abnormalities of mycoplasma pneumonia in children can be seen on a CT scan, and the symptoms can help doctors diagnose patients more accurately 24 . It has been reported that Clinical worsening or mortality in patients with COVID-19 pneumonia can be predicted using a quantitative assessment of the radiological extent and severity of lung illness utilizing an autonomous 3D AI-based software 25 .…”