Department of Renal Medicine, The First Affiliated Hospital of Jiamusi University, Jiamusi, 154002 Heilongjiang, China.
Department of Endocrinology, The First Affiliated Hospital of Jiamusi University, Jiamusi, 154002 Heilongjiang, China.
Comput Math Methods Med. 2022 Mar 10;2022:6440138. doi: 10.1155/2022/6440138. eCollection 2022.
This study was aimed at exploring the effect of ultrasound image evaluation of comprehensive nursing scheme based on artificial intelligence algorithms on patients with diabetic kidney disease (DKD). 44 patients diagnosed with DKD were randomly divided into two groups: group A (no nursing intervention) and group B (comprehensive nursing). In the same period, 32 healthy volunteers were selected as the control group. Ultrasonographic images based on the non-local-means (KNL-Means) filtering algorithm were used to perform imaging examinations in healthy people and DKD patients before and after care. The results suggested that compared with those of the SAE reconstruction algorithm and KAVD reconstruction algorithm, the PSNR value of artificial bee colony algorithm reconstruction of image was higher and the MSE value was lower. The resistant index (RI) of DKD patients in group B after nursing was 0.63 ± 0.06, apparently distinct from the RI of the healthy people (controls) in the same group (0.58 ± 0.06) and the RI of DKD patients in group A (0.68 ± 0.07) ( < 0.05). The incidence rate of complications in DKD patients in group B was apparently inferior to that in group A. After comprehensive nursing intervention (CNI), the scores of all dimensions of quality of life (QoL) in DKD patients in group B were obviously superior versus those in DKD patients in group A. It suggests that implementation of nursing intervention for DKD patients can effectively help patients improve and control the level of renal function, while ultrasound images based on intelligent algorithm can dynamically detect the changes in the level of renal function in patients, which has the value of clinical promotion.
本研究旨在探讨基于人工智能算法的超声影像综合护理方案对糖尿病肾病（DKD）患者的影响。将 44 例 DKD 患者随机分为两组：A 组（无护理干预）和 B 组（综合护理）。同期选择 32 名健康志愿者作为对照组。采用基于非局部均值（KNL-Means）滤波算法的超声图像，对健康人和 DKD 患者护理前后进行成像检查。结果表明，与 SAE 重建算法和 KAVD 重建算法相比，人工蜂群算法重建图像的 PSNR 值更高，MSE 值更低。护理后 B 组 DKD 患者的阻力指数（RI）为 0.63±0.06，明显高于同组健康人（对照组）的 RI（0.58±0.06）和 A 组 DKD 患者的 RI（0.68±0.07）（<0.05）。B 组 DKD 患者的并发症发生率明显低于 A 组。经过综合护理干预（CNI）后，B 组 DKD 患者生活质量（QoL）各维度评分明显优于 A 组。这表明对 DKD 患者实施护理干预可有效帮助患者改善和控制肾功能水平，而基于智能算法的超声图像可动态检测患者肾功能水平的变化，具有临床推广价值。
