Department of Nephrology, Wenzhou Central Hospital, Wenzhou, 325000 Zhejiang, China.
Department of Radiology, Wenzhou Central Hospital, Wenzhou, 325000 Zhejiang, China.
Comput Math Methods Med. 2022 Jul 11;2022:5700249. doi: 10.1155/2022/5700249. eCollection 2022.
This study was aimed to analyze the correlation between blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) images and prognosis of patients with diabetic nephropathy (DN) based on artificial intelligence (AI) segmentation algorithm, so as to provide references for diagnosis and treatment as well as prognosis analysis of patients DN. In this study, a kernel function-based fuzzy C-means algorithm (KFCM) model was proposed, and the FCM algorithm based on neighborhood pixel information (BCFCM) and the FCM algorithm based on efficiency improvement (EnFCM) were introduced for comparison to analyze the image segmentation effects of three algorithms. The results showed that the partition coefficient (V) and partition entropy (V) of the KFCM algorithm were 0.801 and 0.602, respectively, which were better than those of the traditional FCM, BCFCM, and EnFCM algorithm. At the same time, the effects of correlation between renal cortex R2∗ (RC-R2∗), renal medulla R2∗ (RM-R2∗), renal cortex D (RC-D), renal medulla D (RM-D) and renal function on the prognosis were compared. The results showed that the correlation coefficients between RC-R2∗, RM-R2∗, RC-D, RM-D and renal function were 0.57, 0.62, 0.49, and 0.38, respectively; among them, RC-R2∗ and RM-R2∗ were negatively correlated to the estimated glomerular filtration rate (eGFR), and the difference between the groups was statistically significant ( <0.05). Among the factors affecting the prognosis of DN patients, the GFR, hemoglobin (Hb), RC-R2∗, RM-R2∗, and RC-D were all related to the prognosis of DN, and the difference between groups was statistically obvious ( <0.05). It suggested that the KFCM algorithm proposed in this study showed the relatively best segmentation effect on BOLD-MRI images for DN patients; an increase in R2∗ indicated a poor prognosis, and an increase in the RC-D value indicated a better prognosis.
本研究旨在分析基于人工智能（AI）分割算法的血氧水平依赖磁共振成像（BOLD-MRI）图像与糖尿病肾病（DN）患者预后的相关性，为 DN 患者的诊断、治疗及预后分析提供参考。本研究提出了一种基于核函数的模糊 C 均值算法（KFCM）模型，并引入了基于邻域像素信息的模糊 C 均值算法（BCFCM）和基于效率改进的模糊 C 均值算法（EnFCM）进行对比分析，以比较三种算法的图像分割效果。结果表明，KFCM 算法的划分系数（V）和划分熵（V）分别为 0.801 和 0.602，均优于传统 FCM、BCFCM 和 EnFCM 算法。同时，比较了肾皮质 R2∗（RC-R2∗）、肾髓质 R2∗（RM-R2∗）、肾皮质 D（RC-D）、肾髓质 D（RM-D）与肾功能之间的相关性对预后的影响。结果表明，RC-R2∗、RM-R2∗、RC-D、RM-D 与肾功能之间的相关系数分别为 0.57、0.62、0.49、0.38，其中 RC-R2∗和 RM-R2∗与估算肾小球滤过率（eGFR）呈负相关，组间差异有统计学意义（ <0.05）。在影响 DN 患者预后的因素中，GFR、血红蛋白（Hb）、RC-R2∗、RM-R2∗和 RC-D 与 DN 患者的预后均有关，组间差异有统计学意义（ <0.05）。提示本研究提出的 KFCM 算法对 DN 患者的 BOLD-MRI 图像具有较好的分割效果；R2∗增加提示预后不良，RC-D 值增加提示预后较好。
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