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Tic Disorder of Children Analyzed and Diagnosed by Magnetic Resonance Imaging Features under Convolutional Neural
Chunxia Wu1, Qingerile Si1, Budegerile Su1
1Combination of Mongolian and Western Medicine with Pediatrics, Affiliated Hospital of Inner Mongolia University for The Nationalities, Tongliao 028000, Inner Mongolia Autonomous Region, China.
Insights
Convolutional neural networks (CNNs) enhance magnetic resonance imaging (MRI) for diagnosing pediatric tic disorders. This AI-driven approach improves diagnostic speed and accuracy, aiding clinical identification.
Area of Science:
- Neuroimaging
- Artificial Intelligence in Medicine
- Pediatric Neurology
Background:
- Tic disorder diagnosis in children can be challenging.
- Magnetic Resonance Imaging (MRI) offers potential insights into neurobiological underpinnings.
- Developing objective diagnostic tools is crucial for effective treatment.
Purpose of the Study:
- To investigate the utility of Convolutional Neural Networks (CNNs) in analyzing MRI features for tic disorder diagnosis in children.
- To assess the impact of CNNs on MRI reconstruction speed and diagnostic accuracy.
- To correlate MRI findings with clinical severity using the Yale score.
Main Methods:
- Utilized MRI data from 45 children with tic disorder and 30 healthy controls.
- Developed and applied a CNN model for image processing and analysis.
- Analyzed metabolite levels and correlated findings with Yale scores.
Main Results:
- CNNs significantly improved MRI reconstruction speed and diagnostic accuracy.
- Children with tic disorder showed slight, non-statistically significant increases in certain metabolites compared to controls.
- The Yale score indicated a higher proportion of moderate tic disorder severity.
Conclusions:
- MRI combined with CNN algorithms effectively identifies pathological changes in pediatric tic disorder.
- This AI-powered approach provides a valuable reference for clinical diagnosis and identification.
- Further research into metabolite changes may refine diagnostic capabilities.
Abstract:
This work aimed to explore the analysis and diagnosis of children with tic disorder by magnetic resonance imaging (MRI) features under convolutional neural network (CNN), to provide a certain reference basis for clinical identification. A total of 45 children diagnosed with tic disorder in hospital from January 2018 to June 2020 were selected as the research subjects. A total of 30 normal children were selected as the control group. MRI images were collected, and CNN was constructed for image processing. The results showed that the convolutional neural network could significantly improve the speed of MRI reconstruction and can improve the diagnostic accuracy. Compared with normal children, the metabolites in children with tic disorder were slightly increased, but there was no statistical significance (P > 0.05). The results of the Yale score showed that the proportion of children with moderate illness was significantly greater than that of children with mild and severe illness. In short, the pathological changes of tic disorder were effectively discovered by MRI based on CNN algorithms, which can provide a reference for clinical identification.
