Children's Rehabilitation Department, Cang Zhou Women and Children's Healthcare Hospital, Cangzhou, Hebei 061000, China.
Paediatric Internal Medicine Department, Cang Zhou Women and Children's Healthcare Hospital, Cangzhou, Hebei 061000, China.
J Healthc Eng. 2021 Nov 10;2021:1822776. doi: 10.1155/2021/1822776. eCollection 2021.
In this paper, we analyzed the application value and effect of deep learn-based image segmentation model of convolutional neural network (CNN) algorithm combined with 3D brain magnetic resonance imaging (MRI) in diagnosis of cerebral palsy in children. 3D brain model was segmented based on CNN algorithm to obtain the segmented MRI images of brain tissue, and the validity was verified. Then, 70 children with cerebral palsy were rolled into the observation group ( = 35), which received MRI for diagnosis after segmentation of brain tissue, and control group ( = 35), which were diagnosed by computed tomography (CT). The diagnosis results of the two groups were compared. The validity experiment verified that the image segmentation method based on CNN algorithm can obtain effective style graphics. In clinical trials, the diagnostic accuracy of 88.6% in the observation group was evidently superior to that of 80% in the control group ( < 0.05). In the observation group, one patient was diagnosed as normal, four patients had white matter lesions, 17 patients had corpus callosum lesions, and five patients had basal ganglia softening foci. In the control group, two patients were diagnosed as normal, two patients had white matter lesions, 19 patients had corpus callosum lesions, and four patients had basal ganglia softening foci. No notable difference was found between the two groups ( > 0.05). According to the research results, in the diagnosis of cerebral palsy in children, the image segmentation of brain 3D model based on CNN to obtain the MRI image of segmented brain tissue can effectively improve the detection accuracy. Moreover, the specific symptoms can be diagnosed clearly. It can provide the corresponding diagnostic basis for clinical diagnosis and treatment and was worthy of clinical promotion.
在本文中，我们分析了基于卷积神经网络（CNN）算法的深度学习图像分割模型在儿童脑瘫诊断中的应用价值和效果。基于 CNN 算法对 3D 脑模型进行分割，获得脑组织分割的 MRI 图像，并验证其有效性。然后，将 70 例脑瘫患儿纳入观察组（n=35），对其进行脑组织分割后的 MRI 诊断，对照组（n=35）则进行 CT 诊断。比较两组的诊断结果。有效性实验验证了基于 CNN 算法的图像分割方法可以获得有效的样式图形。在临床试验中，观察组的诊断准确率为 88.6%，明显优于对照组的 80%（<0.05）。在观察组中，1 例患者被诊断为正常，4 例患者有白质病变，17 例患者有胼胝体病变，5 例患者有基底节软化灶。在对照组中，2 例患者被诊断为正常，2 例患者有白质病变，19 例患者有胼胝体病变，4 例患者有基底节软化灶。两组间无显著差异（>0.05）。根据研究结果，在儿童脑瘫的诊断中，基于 CNN 对 3D 脑模型进行图像分割以获得分割后的脑组织 MRI 图像，可以有效提高检测准确率。此外，还可以明确诊断出具体症状。可为临床诊断和治疗提供相应的诊断依据，值得临床推广。
