Implementation of hospital-to-home model for nutritional nursing management of patients with chronic kidney disease using artificial intelligence algorithm combined with CT internet +
Open accessAbstract
The objective of this study was to evaluate the application value of “Internet + hospital‐to‐home (H2H)” nutritional care model using the improved wavelet transform algorithm based on computed tomography (CT) images in the nutritional care management of chronic kidney disease (CKD) stages 3‐5. A total of 120 patients with CKD were the research objects and they were randomly divided into two groups. The normal nutritional nursing model was used for nursing of patients in the control group, and the “Internet + H2H″ model was used for the observation group (H2H group), with 60 cases in each group. The nursing effect was evaluated using 320‐slice volume CT low‐dose perfusion imaging images, anthropometry, laboratory biochemical tests, and other survey scores. The results showed that compared with the mean filter denoising (MFD) algorithm and the orthogonal wavelet denoising (OWD) algorithm, the mean square error (MSE) and signal noise ratio (SNR) values of the IWT algorithm were better (40.0781 vs 45.2891, 59.2123)/(20.0122 vs 18.2311, 15.7812) (P < 0.05). The arm muscle circumference (MAC) (239.77 ± 18.24 vs 243.94 ± 18.72 mm) and triceps skindold (TSF) value (8.87 ± 2.74 vs 10.04 ± 2.90 mm) of the patients in the H2H group were greatly improved after the nursing (P < 0.05). For biochemical indicators, serum albumin (ALB) (35.22 ± 4.98 vs 45.32 ± 4.21) g/L, prealbumin (PAB) (289.94 ± 72.99 vs 341.79 ± 74.45) mg/L, hemoglobin (Hb) (97.62 ± 24.87 vs 110.65 ± 28.83) g/L, and blood urea nitrogen (BUN) (15.74 ± 9.87 vs 11.06 ± 5.69) mmol/L of patients in H2H group were improved (P < 0.05). After nursing, the nutritional screening score of the H2H group was obviously improved (83.33% (before) vs 50% (after)), the total score of health quality assessment (114.89 ± 5.23) in the H2H group was much higher than that of the control group (87.22 ± 14.89), and the satisfaction on the nursing model was higher in the H2H group (100% vs 71.67%) (P < 0.05). The renal cortex BF before and after nursing was significantly different between the two groups of patients (P < 0.05), and the BE of the H2H group was significantly higher than that of the control group after treatment ((335.12 ± 52.74) mL·100 g−1·min−1 vs (289.90 ± 53.91) mL·100 g−1·min−1) (P < 0.05). In summary, the “Internet + H2H″ nutritional nursing model was more individualized, which can better improve the physical quality of patients with stages 3‐5 of CKD, improve the psychological state of patients, and further enhance the prognosis of the disease. In addition, the IWT algorithm showed better effects in the processing of the image of 320‐slice volume CT low‐dose perfusion imaging, and it was worthy of clinical application.
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Chen et al. (47) implemented an AI-based "Internet + Hospital-to-Home" (H2H) nutritional nursing model for chronic kidney disease (CKD) patients; this model optimized nutritional assessments and monitoring through automated data analysis and personalized care plans, reducing manual assessment time and enhancing resource allocation based on real-time patient needs.
“Similarly, Chen et al. (47) implemented an AI-based “Internet + Hospital-to-Home” (H2H) nutritional nursing model for chronic kidney disease (CKD) patients. This model optimized nutritional assessments and monitoring through automated data analysis and personalized care plans, reducing manual assessment time and enhancing resource allocation based on real-time patient needs.”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [47]
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Artificial intelligence in nursing: an integrative review of clinical and operational impacts
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- Authors
- Chen, X., Huang, X., & Yin, M.
- Affiliations
- Hunan Provincial People's Hospital (CN)
- Corresponding author
- Yin, Mingyuan
- Venue
- Contrast Media & Molecular Imaging
- Publisher
- Wiley
- Published
- 2022-01
- Volume
- 2022
- Issue
- 1
- Pages
- 1183988-1183988
- ISSN
- 1555-4309 (ISSN-L)
- 1555-4317
- PMC
- PMC8977294
- OpenAlex
- W4220676558
- Cited by
- 25
- References
- 32
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 4 · 2023: 6 · 2022: 2
- FWCI
- 3.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Contrast Media & Molecular Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Scientific Research Project of Hunan Provincial Health Commission (202114010632)
- Topics
- Health Sciences › Medicine › Nephrology › Dialysis and Renal Disease Management
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Health Sciences › Health Professions › Health Information Management › Dietetics, Nutrition, and Education
- Keywords
- hospital-to-home care, chronic kidney disease, Internet Plus, wavelet denoising, CT perfusion, image denoising, arm muscle circumference, triceps skinfold thickness, serum albumin, prealbumin, hemoglobin, blood urea nitrogen, telemedicine, nutritional assessment, protein-energy wasting
- MeSH terms
- Algorithms, Artificial Intelligence, Hospitals, Humans, Nutritional Status, Tomography, X-Ray Computed, Nutrition Assessment, Internet, Renal Insufficiency, Chronic
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