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基于智能分割算法联合血清学指标对脑出血后血肿扩大的早期脑 CT 预测。

Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage.

机构信息

Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001 Heilongjiang, China.

First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150040 Heilongjiang, China.

出版信息

Comput Math Methods Med. 2022 Jun 14;2022:5863082. doi: 10.1155/2022/5863082. eCollection 2022.

Abstract

The aim of this study was to explore the application value of brain computed tomography (CT) images under intelligent segmentation algorithm and serological indexes in the early prediction of hematoma enlargement in patients with intracerebral hemorrhage (ICH). Fuzzy -means (FCM) intelligence segmentation algorithm was introduced, and 150 patients with early ICH were selected as the research objects. Patient cerebral CT images were intelligently segmented to assess the diagnostic value of this algorithm. According to different hematoma volumes during CT examination, patients were divided into observation group (hematoma enlargement occurred, = 48) and control group (no hematoma enlargement occurred, = 102). The predicative value of hematoma enlargement after ICH was investigated by assessing CT image quality and measuring intracerebral edema, hematoma volume, and serological indicators of the patients of the two groups. The results demonstrated that the sensitivity, specificity, and accuracy of CT images processed by intelligence segmentation algorithm amounted to 0.894, 0.898, and 0.930, respectively. Besides, early edema enlargement and hematoma of patients in the observation group were more significant than those of patients in the control group. Relative edema volume was 0.912, which was apparently lower than that in the control group (1.017) ( < 0.05). In terms of CT signs of ICH patients, the incidence of blend sign, low density sign, and stroke of the observation group was evidently higher than those of the control group ( < 0.05). Besides, absolute lymphocyte count (ALC) and hemoglobin (HGB) concentration of the patients in the observation group were 6.23 × 109/L and 6.29 × 109/L, respectively, both of which were higher than those of the control group (6.08 × 109/L and 4.25 × 109/L). Neutrophil to lymphocyte ratio (NLR) was 0.99 × 109/L, which was apparently lower than that in the control group (1.43 × 109/L) ( < 0.05). To sum up, cerebral CT images processed by FCM algorithm showed good diagnostic effect on ICH and high clinical values in the early prediction of hematoma among ICH patients.

摘要

本研究旨在探讨智能分割算法下脑计算机断层扫描(CT)图像与血清学指标在预测脑出血(ICH)患者血肿扩大中的应用价值。引入模糊均值(FCM)智能分割算法,选取 150 例早期 ICH 患者作为研究对象,对患者颅脑 CT 图像进行智能分割,评估该算法的诊断价值。根据 CT 检查时不同的血肿量,将患者分为观察组(血肿扩大发生,n=48)和对照组(血肿未扩大发生,n=102)。评估两组患者 CT 图像质量并测量颅内水肿、血肿量及血清学指标,探讨 ICH 后血肿扩大的预测价值。结果表明,智能分割算法处理后的 CT 图像灵敏度、特异度和准确度分别为 0.894、0.898 和 0.930。此外,观察组患者早期水肿扩大和血肿比对照组更明显。相对水肿体积为 0.912,明显低于对照组(1.017)( < 0.05)。在 ICH 患者 CT 征象方面,观察组混合征、低密度征和中风的发生率明显高于对照组( < 0.05)。此外,观察组患者的绝对淋巴细胞计数(ALC)和血红蛋白(HGB)浓度分别为 6.23×109/L 和 6.29×109/L,均高于对照组(6.08×109/L 和 4.25×109/L)。中性粒细胞与淋巴细胞比值(NLR)为 0.99×109/L,明显低于对照组(1.43×109/L)( < 0.05)。综上所述,FCM 算法处理后的脑 CT 图像对 ICH 具有良好的诊断效果,在预测 ICH 患者血肿方面具有较高的临床价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb4e/9213170/dac0bf36acba/CMMM2022-5863082.001.jpg

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