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      Doppler Ultrasound under Image Denoising Algorithm in the Diagnosis and Treatment of Fetal Growth Restriction Using Aspirin Combined with Low-Molecular-Weight Heparin

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      Journal of Healthcare Engineering
      Hindawi

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          Abstract

          Objective

          This study explored the clinical application value of image denoising algorithm combined with Doppler ultrasound imaging in evaluation of aspirin combined with low-molecular-weight heparin (LMWH) on fetal growth restriction (FGR).

          Method

          A two-stage image denoising by principal component analysis (PCA) with local pixel grouping (LPG-PCA) denoising algorithm was constructed in this study. Eighty FGR pregnant women were included in the study, and they were rolled into an experimental group (aspirin enteric-coated tablets + LMWH calcium injection) and a control group (LMWH calcium injection) according to the different treatment plans, with 40 cases in each group. All patients were performed with Doppler ultrasound imaging. The blood flow parameters (BFPs) were recorded and compared before and after the treatment in two groups, including power index (PI), resistance index (RI), high systolic blood flow velocity (S), high diastolic blood flow velocity (D), S/D value, and peak systolic velocity (PSV). In addition, the middle cerebral artery (MCA) BFPs, cerebral placental rate (CPR), amniotic fluid index (AFI) and perinatal outcome (PO) of the two groups were compared.

          Results

          The total effective rate of treatment in group A (87.5%) was greatly higher than that in group B (62.5%), showing statistical difference ( P < 0.05). The PI (0.72 ± 0.19), RI (0.57 ± 0.17), and S/D values (2.26 ± 0.43) in group A were dramatically lower than those in group B, which were 0.92 ± 0.21, 0.75 ± 0.14, and 2.64 ± 0.45, respectively ( P < 0.05), and the AFI was higher (13.71 ± 2.2 cm vs 11.38 ± 2.16 cm) ( P < 0.05). The Apgar score (9.17 ± 0.26), weight (3.57 ± 1.08), and gestational age (38.85 ± 2.50) of group A were all higher in contrast to those of group B, which were 7.33 ± 0.25, 2.61 ± 1.13, and 36.18 ± 2.25, respectively ( P < 0.05). In addition, the fetal double parietal diameter (2.4 ± 0.9 mm), femur diameter (2.2 ± 0.6 mm), head circumference (1.2 ± 0.4 mm), abdominal circumference (1.3 ± 0.7 mm), and uterine height (0.8 ± 0.3 mm) in group A were obviously superior to those in group B, which were 1.8 ± 0.4 mm, 1.7 ± 0.5 mm, 0.8 ± 0.2 mm, 0.9 ± 0.4 mm, and 0.4 ± 0.6 mm, respectively, showing statistically observable differences ( P < 0.05).

          Conclusion

          Doppler ultrasound based on image denoising algorithm can accurately evaluate the effect of aspirin combined with LMWH on the improvement of FGR and showed good application value.

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          Most cited references19

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          Fuzzy System Based Medical Image Processing for Brain Disease Prediction

          The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.
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            Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

            The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.
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              Multi-Disease Prediction Based on Deep Learning: A Survey

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                Author and article information

                Contributors
                Journal
                J Healthc Eng
                J Healthc Eng
                JHE
                Journal of Healthcare Engineering
                Hindawi
                2040-2295
                2040-2309
                2021
                16 October 2021
                : 2021
                : 9697962
                Affiliations
                Department of Obstetrics, Huai'an Maternity and Child Care Hospital, Huaian 223001, Jiangsu, China
                Author notes

                Academic Editor: Chinmay Chakraborty

                Author information
                https://orcid.org/0000-0003-1579-1207
                https://orcid.org/0000-0002-1929-047X
                https://orcid.org/0000-0001-9497-9958
                Article
                10.1155/2021/9697962
                8541844
                34697569
                4ca273f6-04fc-4ed9-b0eb-2825f7df6f1d
                Copyright © 2021 Huiling Liu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 3 August 2021
                : 27 September 2021
                Categories
                Research Article

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