Deep Learning-Based MR Imaging for Analysis of Relation between Cerebrospinal Fluid Variation and Communicating Hydrocephalus after Decompressive Craniectomy for Craniocerebral Injury - Mao, Yangming; Shen, Zhouming; Wang, Jun; Zhu, Haifeng; Yu, Zhengyong; Chen, Xiang; Cheng, Hua (Scientific programming, 2022)
The aim of this study was to analyze the relationship between cerebrospinal fluid variation and hydrocephalus through deep learning-based (DL-based) magnetic resonance imaging (MRI), in order to improve the postoperative recovery of craniocerebral injury (CI) patients and reduce the deterioration after decompressive craniectomy of the patient. 50 patients with CI who received the diagnosis and treatment at the hospital were chosen as the subjects. The retrospective analysis on patients with CI was conducted herein. First, the general clinical data of the patients were analyzed. Next, the MRI images for brain damage of the patients were collected. After that, the DL-based brain image classification and the artificial intelligence technology were utilized to solve the classification problems with classifiers. Finally, the DL-based convolutional neural network (CNN) was adopted to preprocess the image features, and the offline training and online reconstruction were conducted after the construction of the model. Four groups of databases were selected for deep learning and analyzed in terms of data feature analysis, principal component analysis (PCA), and application of 3D scale-invariant feature transformation (SIFT) operators in image data analysis. The results manifested that, the WHGO descriptor and the area under the curve were the largest. SIFI, PCA, and WHGO had the sensitivity of 87.5%, 88.2%, and 90.1%, the specificity of 91.8%, 90.1%, and 94.2%, and the correct rate of 90.6%, 87.5%, and 92.4%, respectively. The volume content of the cerebrospinal fluid in the subarachnoid space was 77.04% higher than that of cerebrospinal fluid in the ventricle. In conclusion, the intelligent deep learning model proposed in this article was able to assume the measurement of clinical cases and auxiliary diagnosis. This model not only can help doctors save the diagnosis time and enhance the medical efficiency, but also can precisely identify the patient’s lesion area. Therefore, it had substantial application potential in mobile-based clinical practice.