Meng Kangkang, Ying Chao, Ji Jianwei, Yang Lianfang
Kangkang Meng, Attending Physician. Department of Neonatology, Yiwu Central Hospital, Yiwu, 322000, China.
Chao Ying, Attending Physician. Department of Neonatology, Yiwu Central Hospital, Yiwu, 322000, China.
Pak J Med Sci. 2021;37(6):1682-1686. doi: 10.12669/pjms.37.6-WIT.4883.
To explore the diagnostic effect of ultrasound imaging on the illness severity, and to analyze neurobehavioral development of neonates with Infectious Pneumonia (IPN), Self- Adaptation (SD), and Spatial Smoothing (SS) technologies were adopted to build SDSS. Then, the WFFSF algorithm based on Wiener Filtering (WF) and Feature Space Fusion (FSF) and the SNRP-FSF algorithm based on Signal-to-noise ratio post-filtering (SNRP) and FSF were introduced for comparison.
One hundred and thirty-two neonates were divided into group without respiratory failure (S1) and respiratory failure group (S2). The study was conducted from March 2018 to July 2020. According to scoring systems for neonatal critically illness, they were divided into non-severe group (W1), severe group (W2), and extremely-severe group (W3). According to the Scale of Child Development Center of China (CDCC), they were divided into a normal neurobehavioral developmental group (P1) and an abnormal neurobehavioral developmental group (P2).
The normalized mean square distance l and normalized mean absolute distance f of SDSS algorithm were significantly lower than that of WFFSF algorithm and SNRP-FSF algorithm, and the peak signal-to-noise ratio (PSNR) was significantly higher than that of WFFSF algorithm and SNRP-FSF algorithm (P<0.05). The lung ultrasound score (40.62±7.22%) of S1 was greatly higher than S2 group (28.47±6.29%) (P<0.05); the lung ultrasound score (39.13±8.25) in W1 was greatly higher than W2 (27.28±6.39) and W3 groups (14.33±7.03); neonates in group W2 had higher lung ultrasound scores than W3 (P<0.05), and lung ultrasound scores in P1 (42.57±8.58) was greatly higher than that the P2 group (26.49±6.09).
In contrast with traditional algorithms, the SDSS algorithm based on AD has a better reconstruction effect on neonatal IPN ultrasound images. The lung ultrasound score can clearly indicate the severity of the disease and neurobehavioral development of neonate IPN, and the lung ultrasound score is negatively correlated with the severity of the child's disease and the abnormality of neurobehavioral development.
探讨超声成像对疾病严重程度的诊断效果，分析感染性肺炎（IPN）新生儿的神经行为发育情况，采用自适应（SD）和空间平滑（SS）技术构建SDSS。然后，引入基于维纳滤波（WF）和特征空间融合（FSF）的WFFSF算法以及基于信噪比后滤波（SNRP）和FSF的SNRP-FSF算法进行比较。
将132例新生儿分为无呼吸衰竭组（S1）和呼吸衰竭组（S2）。研究于2018年3月至2020年7月进行。根据新生儿危重症评分系统，将他们分为非重症组（W1）、重症组（W2）和极重症组（W3）。根据中国儿童发展中心量表（CDCC），将他们分为神经行为发育正常组（P1）和神经行为发育异常组（P2）。
SDSS算法的归一化均方距离l和归一化平均绝对距离f显著低于WFFSF算法和SNRP-FSF算法，峰值信噪比（PSNR）显著高于WFFSF算法和SNRP-FSF算法（P<0.05）。S1组的肺部超声评分（40.62±7.22%）显著高于S2组（28.47±6.29%）（P<0.05）；W1组的肺部超声评分（39.13±8.25）显著高于W2组（27.28±6.39）和W3组（14.33±7.03）；W2组新生儿的肺部超声评分高于W3组（P<0.05），P1组的肺部超声评分（42.57±8.58）显著高于P2组（26.49±6.09）。
与传统算法相比，基于AD的SDSS算法对新生儿IPN超声图像具有更好的重建效果。肺部超声评分能够清晰地表明新生儿IPN的疾病严重程度和神经行为发育情况，且肺部超声评分与患儿疾病严重程度及神经行为发育异常呈负相关。
