Department of Infusion Room of Emergency, Children's Hospital of Nanjing Medical University, Nanjing, 210000 Jiangsu Province, China.
Comput Math Methods Med. 2022 Jun 17;2022:5909922. doi: 10.1155/2022/5909922. eCollection 2022.
Its goal was to see how convolutional neural network- (CNN-) based superresolution (SR) technology magnetic resonance imaging- (MRI-) assisted transition care (TC) affected the prognosis of children with severe viral encephalitis (SVE) and how effective it was.
90 SVE children were selected as the research objects and divided into control group (39 cases receiving conventional nursing intervention) and observation group (51 cases performed with conventional nursing intervention and TC intervention) according to their nursing purpose. Based on SR-CNN-optimized MRI images, diagnosis was implemented. Life treatment and sequelae in two groups were compared.
After the processing by CNN algorithm-based SR, peak signal to noise ratio (PSNR) (40.08 dB) and structural similarity (SSIM) (0.98) of MRI images were both higher than those of fully connected neural network (FNN) (38.01 dB, 0.93) and recurrent neural network (RNN) (37.21 dB, 0.93) algorithms. Diagnostic sensitivity (95.34%), specificity (75%), and accuracy (94.44%) of MRI images were obviously superior to those of conventional MRI (81.40%, 50%, and 80%). PedsQLTM 4.0 scores of the observation group 1 to 3 months after discharge were all higher than those of the control group (54.55 ± 5.76 vs. 52.32 ± 5.12 and 66.32 ± 8.89 vs. 55.02 ± 5.87). Sequela incidence in the observation group (13.73%) was apparently lower than that in the control group (43.59%) ( < 0.05).
(1) SR-CNN algorithm could increase the definition and diagnostic ability of MRI images. (2) TC could reduce sequelae incidence among SVE children and improve their quality of life (QOL).
观察基于卷积神经网络（CNN）的超分辨率（SR）技术磁共振成像（MRI）辅助过渡护理（TC）对重症病毒性脑炎（SVE）患儿预后的影响及其效果。
选取 90 例 SVE 患儿为研究对象，根据护理目的分为对照组（39 例行常规护理干预）和观察组（51 例行常规护理干预+TC 干预）。基于 SR-CNN 优化的 MRI 图像进行诊断，比较两组患儿的生活治疗及后遗症情况。
CNN 算法优化后的 MRI 图像的峰值信噪比（PSNR）（40.08dB）和结构相似性（SSIM）（0.98）均高于全连接神经网络（FNN）（38.01dB，0.93）和循环神经网络（RNN）（37.21dB，0.93）算法。MRI 图像的诊断灵敏度（95.34%）、特异度（75%）和准确度（94.44%）明显优于常规 MRI（81.40%、50%、80%）。观察组出院后 1~3 个月的 PedsQLTM4.0 评分均高于对照组[（54.55±5.76）分比（52.32±5.12）分、（66.32±8.89）分比（55.02±5.87）分]。观察组后遗症发生率（13.73%）明显低于对照组（43.59%）（<0.05）。
（1）SR-CNN 算法可提高 MRI 图像的清晰度和诊断能力。（2）TC 可降低 SVE 患儿后遗症发生率，提高其生活质量。
