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Magnetic Resonance Imaging Manifestations of Pediatric Purulent Meningitis Based on Immune Clustering Algorithm
Dafei Wei1, Pan He1, Qian Guo1
1Department of Pediatrics, The Second Affiliated Hospital of Nanhua University, Hengyang 421000, Hunan, China.
Abstract:
The purpose of this study was to analyze the diagnostic value of magnetic resonance imaging (MRI) based on the immune clustering algorithm (ICA) in children with purulent meningitis. In this study, 235 children with suspected pediatric purulent meningitis (PPM) were routinely scanned, and the artificial immune algorithm (AIA) and ICA were applied to image processing. In order to quantitatively analyze the accuracy and precision of the processed image, precision rate was introduced as the evaluation of accuracy, and the True Positive Vis Fox, False Negative Vis Fo, and False Positive Vis Fo were selected as the evaluation indicators. After comparison, the accuracy, sensitivity, and specificity of ICA detection were higher than those of AIA and conventional plain scanning, and the differences were statistically obvious (P < 0.05). Comparison on image display effects showed that compared with AIA, the image processed by the ICA algorithm constructed in this study showed the highest definition and contrast and the best denoising effect and image quality, showing a statistically obvious difference (P < 0.05). All in all, the display effect of MRI images of pediatric purulent meningitis based on ICA was more accurate and clearer than that of the traditional image processing, and it can provide a more accurate auxiliary basis in the diagnosis of lesion details. It also showed a higher clinical value for the development of a diagnosis and treatment plan for complicated PPM.
Insights
Magnetic resonance imaging (MRI) enhanced with the immune clustering algorithm (ICA) significantly improves the diagnosis of pediatric purulent meningitis (PPM). ICA offers superior accuracy, sensitivity, and image quality compared to traditional methods.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Pediatric Neurology
Background:
- Pediatric purulent meningitis (PPM) is a serious condition requiring accurate and timely diagnosis.
- Traditional diagnostic methods for PPM, including conventional MRI, may have limitations in image clarity and diagnostic precision.
- Advanced image processing algorithms are needed to enhance the diagnostic value of MRI in pediatric neuroinfections.
Purpose of the Study:
- To evaluate the diagnostic performance of magnetic resonance imaging (MRI) utilizing the immune clustering algorithm (ICA) for pediatric purulent meningitis (PPM).
- To compare the accuracy, sensitivity, specificity, and image quality of ICA-processed MRI with artificial immune algorithm (AIA) and conventional scanning.
- To determine the clinical utility of ICA-based MRI in diagnosing lesion details and guiding treatment plans for PPM.
Main Methods:
- A cohort of 235 children with suspected PPM underwent routine MRI scans.
- Image processing was performed using the artificial immune algorithm (AIA) and the immune clustering algorithm (ICA).
- Quantitative analysis of diagnostic accuracy and image quality included precision rate, True Positive Vis Fox, False Negative Vis Fo, and False Positive Vis Fo.
Main Results:
- ICA-based MRI demonstrated statistically significant higher accuracy, sensitivity, and specificity (P < 0.05) compared to AIA and conventional plain scanning.
- Images processed by ICA exhibited superior definition, contrast, and denoising effects, resulting in enhanced image quality (P < 0.05) over AIA.
- ICA-based MRI provided clearer and more accurate visualization of lesion details, offering a better auxiliary basis for diagnosis.
Conclusions:
- The immune clustering algorithm (ICA) significantly enhances the diagnostic value of MRI for pediatric purulent meningitis (PPM).
- ICA-based MRI offers improved accuracy, sensitivity, specificity, and image quality, surpassing traditional methods and AIA.
- This advanced imaging technique holds considerable clinical value for accurate diagnosis and effective treatment planning in complicated PPM cases.
