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. 2022 Aug 16:2022:8930584.
doi: 10.1155/2022/8930584. eCollection 2022.

Artificial Intelligence Algorithm-Based MRI in the Diagnosis of Complications after Renal Transplantation

Hang Liu  1 , Liang Ren  1 , Bohan Fan  1 , Wei Wang  1 , Xiaopeng Hu  1 , Xiaodong Zhang  1
Affiliations

Artificial Intelligence Algorithm-Based MRI in the Diagnosis of Complications after Renal Transplantation

Hang Liu et al. Contrast Media Mol Imaging. .

Abstract

This study was to explore the diagnostic value of magnetic resonance imaging (MRI) optimized by residual segmentation attention dual channel network (DRSA-U-Net) in the diagnosis of complications after renal transplantation and to provide a more effective examination method for clinic. 89 patients with renal transplantation were selected retrospectively, and all underwent MRI. The patients were divided into control group (conventional MRI image diagnosis) and observation group (MRI image diagnosis based on DRSA-U-Net). The accuracy of MRI images in the two groups was evaluated according to the comprehensive diagnostic results. The root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) of DRSA-U-Net on T1WI and T2WI sequences were better than those of U-Net and dense U-Net (P < 0.05); comprehensive examination showed that 39 patients had obstruction between ureter and bladder anastomosis, 13 cases had rejection, 10 cases had perirenal hematoma, 5 cases had renal infarction, and 22 cases had no complications; the diagnostic sensitivity, specificity, accuracy, and consistency of the observation group were higher than those of the control group (P < 0.05). In the control group, the sensitivity, specificity, and accuracy in the diagnosis of complications after renal transplantation were 66.5%, 84.1%, and 78.32%, respectively; in the observation group, the sensitivity, specificity, and accuracy in the diagnosis were 67.8%, 86.7%, and 80.6%, respectively. DRSA-U-Net denoising algorithm can clearly display the information of MRI images on the kidney, ureter, and surrounding tissues, improve its diagnostic accuracy in complications after renal transplantation, and has good clinical application value.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Analysis model of DRSA-U-Net network data.
Figure 2
Figure 2
Comparison of RMSE and PSNR of different networks in T1WI. (a) Synthesis of T1WI RMSE. (b) Synthesis of T1WI PSNR. ∗Compared with U-Net, P < 0.05.
Figure 3
Figure 3
Comparison of RMSE and PSNR of different networks in T2WI. (a) Synthesis of T2WI RMSE. (b) Synthesis of T2WI PSNR. ∗Compared with U-Net (P < 0.05).
Figure 4
Figure 4
T2WI synthesized by three networks at 1/4 downsampling T2WI. The blue box indicates the PSNR and RMSE calculated from the current image and the real T2WI.
Figure 5
Figure 5
Examination results of 89 patients.
Figure 6
Figure 6
Comparison of diagnostic sensitivity, specificity, and accuracy between the control group and observation group.
See this image and copyright information in PMC

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