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Multicenter Study
. 2022 Jul 11:2022:5700249.
doi: 10.1155/2022/5700249. eCollection 2022.

Correlation between Blood Oxygen Level-Dependent Magnetic Resonance Imaging Images and Prognosis of Patients with Multicenter Diabetic Nephropathy on account of Artificial Intelligence Segmentation Algorithm

Yifan Zhang  1 , Xiaohan Wang  2 , Zhaoyu Lin  3 , Guojian Shao  1 , Renban Wang  1 , Zhoutao Xie  1
Affiliations
Multicenter Study

Correlation between Blood Oxygen Level-Dependent Magnetic Resonance Imaging Images and Prognosis of Patients with Multicenter Diabetic Nephropathy on account of Artificial Intelligence Segmentation Algorithm

Yifan Zhang et al. Comput Math Methods Med. .

Abstract

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 (Vpc) and partition entropy (Vpe) 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 (P <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 (P <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.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flow chart of FCM algorithm.
Figure 2
Figure 2
Statistics of general clinical data of patients. ∗Compared with DM group, P <0.05.
Figure 3
Figure 3
Statistics of biochemical indicators of patients. Hb unit was g/L, GHb unit was %, Cr unit was μml/L, BUN unit was mmol/L, and eGFR unit was mL/min/1.73 m2. ∗Compared with DM group, P <0.05.
Figure 4
Figure 4
Comparison on the segmentation effects of the three optimized algorithms.
Figure 5
Figure 5
Imaging data of a 45-year-old male patient with left kidney disease. A was the original BOLD-MRI scan, and B was the image segmented by the KFCM algorithm.
Figure 6
Figure 6
Comparison on RC-R2∗ and RC-R2∗ values. A: values of DM group; B: values of DN group; C: comparison on MCR value. ∗Compared with DM group, P <0.05.
Figure 7
Figure 7
Correlation analysis between BOLD-MRI and renal function. ∗Compared with eGFR, P <0.05.
See this image and copyright information in PMC

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References

    1. Feng Y. Z., Ye Y. J., Cheng Z. Y., et al. Non-invasive assessment of early stage diabetic nephropathy by DTI and BOLD MRI. The British Journal of Radiology . 2020;93(1105, article 20190562) doi: 10.1259/bjr.20190562. - DOI - PMC - PubMed
    1. Li Z. C., Cai Y. Z., Tang Z. G., Zuo P. L., Liu R. B., Liu F. Lipo-prostaglandin E1 improves renal hypoxia evaluated by BOLD-MRI in patients with diabetic kidney disease. Clinical Imaging . 2018;50:239–242. doi: 10.1016/j.clinimag.2018.04.015. - DOI - PubMed
    1. Hua F. New insights into diabetes mellitus and its complications: a narrative review. Annals of translational medicine . 2020;8(24):p. 1689. doi: 10.21037/atm-20-7243. - DOI - PMC - PubMed
    1. Yin W. J., Liu F., Li X. M., et al. Noninvasive evaluation of renal oxygenation in diabetic nephropathy by BOLD-MRI. European Journal of Radiology . 2012;81(7):1426–1431. doi: 10.1016/j.ejrad.2011.03.045. - DOI - PubMed
    1. Wang Z. J., Kumar R., Banerjee S., Hsu C. Y. Blood oxygen level-dependent (BOLD) MRI of diabetic nephropathy: preliminary experience. Journal of Magnetic Resonance Imaging . 2011;33(3):655–660. doi: 10.1002/jmri.22501. - DOI - PMC - PubMed

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