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. 2021 Nov 16:2021:7096286.
doi: 10.1155/2021/7096286. eCollection 2021.

Artificial Intelligence Algorithm-Based Computed Tomography Images in the Evaluation of the Curative Effect of Enteral Nutrition after Neonatal High Intestinal Obstruction Operation

Yanqing Dong  1 , Zhaolong Wang  1 , Zhiguang Zhang  1 , Bobo Niu  1 , Pan Chen  1 , Pengju Zhang  1 , Huizhong Niu  1
Affiliations

Artificial Intelligence Algorithm-Based Computed Tomography Images in the Evaluation of the Curative Effect of Enteral Nutrition after Neonatal High Intestinal Obstruction Operation

Yanqing Dong et al. J Healthc Eng. .

Retraction in

Abstract

In this study, CT image technology based on level set intelligent segmentation algorithm was used to evaluate the postoperative enteral nutrition of neonatal high intestinal obstruction and analyze the clinical treatment effect of high intestinal obstruction, so as to provide a reasonable research basis for the clinical application of neonatal high intestinal obstruction. 60 children with high intestinal obstruction treated in the hospital were selected as the research objects. Based on the postoperative enteral nutrition treatment, they were divided into control group (noncatheterization group)-parenteral nutrition support. In the observation group, gastric tube was placed through nose for nutritional support. Then, CT images based on level set segmentation algorithm were used to compare the intestinal recovery of the two groups, and the biochemical indexes and hospitalization were compared. The level set algorithm can accurately segment the lesions in CT images. The segmentation time of the level set algorithm was shorter than that of the traditional algorithm (24.34 ± 2.01 s vs. 75.21 ± 5.91 s), and the segmentation accuracy was higher than that of the traditional algorithm (84.71 ± 3.91% vs. 70.04 ± 3.71%, P < 0.05). The weight of children in the observation group (100 ± 7 g) was higher than that in the control group (54 ± 5 g), and the ICU monitoring time (12.01 ± 2.65 days) and the hospital stay (17.82 ± 3.11 days) were shorter than those in the control group (13.42 ± 2.95 days, 19.13 ± 3.22 days, all P < 0.05). The level set segmentation algorithm can accurately segment the CT image, so that the disease location and its contour can be displayed more clearly. Moreover, the nasal placement of jejunal nutrition tube can effectively improve the intestinal function of children, maintain the steady-state environment of intestinal bacterial growth, and significantly improve the clinical treatment effect, which is worthy of clinical application and promotion.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Schematic diagram of the level set algorithm flow.
Figure 2
Figure 2
CT image examination results under the level set algorithm. (a) Original image. (b) DR-LS algorithm.
Figure 3
Figure 3
Comparison on level set algorithm and traditional algorithm. (a) Segmentation time. (b) Segmentation accuracy. ∗ indicates that the comparison is statistically significant (P < 0.05).
Figure 4
Figure 4
CT imaging examination results of one child patient (the red arrow indicates the original lesion).
Figure 5
Figure 5
Comparison on the situation of all the children' patients after treatment. (a) One-week postoperative weight gains. (b) ICU monitoring time. (c) Postoperative hospital stay. ∗ indicates that the comparison is statistically significant (P < 0.05).
Figure 6
Figure 6
The results of routine blood tests in children patients. (a) Total protein. (b) Albumin. (c) Hemoglobin.
See this image and copyright information in PMC

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