Deep Neural Network-Based Novel Mathematical Model for 3D Brain Tumor Segmentation.

The use of multimodal magnetic resonance imaging (MRI) to autonomously segment brain tumors and subregions is critical for accurate and consistent tumor measurement, which can help with detection, care planning, and evaluation. This research is a contribution to the neuroscience research. In the present work, we provide a completely automated brain tumor segmentation method based on a mathematical model and deep neural networks (DNNs). Each slice of the 3D picture is enhanced by the suggested mathematical model, which is then sent through the 3D attention U-Net to provide a tumor segmented output. The study includes a detailed mathematical model for tumor pixel enhancement as well as a 3D attention U-Net to appropriately separate the pixels. On the BraTS 2019 dataset, the suggested system is tested and verified. This proposed work will definitely help for the treatment of the brain tumor patient. The pixel level accuracy for tumor pixel segmentation is 98.90%. The suggested system architecture's outcomes are compared to those of current system designs. This study also examines the suggested system architecture's time complexity on various processing units with neuroscience approach.

Force Platform-Based Intervention Program for Individuals Suffering with Neurodegenerative Diseases like Parkinson.

The term "neurodegenerative disease" refers to a set of illnesses that primarily affect brain's neurons. Substantia nigra (a midbrain dopaminergic nucleus) with lack of hormone called dopamine causes Parkinson's disease (PD), a neurological disorder. PD leads to tremor, stiffness, impaired posture and balance, and loss of automatic movements. Patient with Parkinson's often develops a parkinsonian gait that includes a tendency to lean forward, small quick steps as if hurrying forward, and reduced swinging of the arms. They also may have trouble initiating or continuing movement. Gait analysis is often used to diagnose neurodegenerative illnesses and determine their stage. In this study, we attempt to investigate postural balance, and of gait signals for Parkinson's patients, also, we incorporate interim rehabilitation technique. We included 25 PD patients who had 2.5 to 3 IV score of Hoehn and Yahr scale. A ten-minute walk test has been performed to observe primary and secondary results of dual task interference on gait velocities, and gait time motion vector for right and left legs was observed. Two experimental ground conditions include three conditions of trunk alignment, that is, erect on a regular basis (RE), trunk dorsiflexion 30° (TF1), and trunk dorsiflexion 50° (TF2) were analysed. We identified the walking speed of PD patients was decreased, and trunk dorsiflexion variables influence the gait pattern of Parkinson's disease patients, where higher 95% CI for TF1 condition was reported. The regular erect trunk showed swing time reduction (0.7%) in PD, so the higher unified PD rating scale (UPDRS) values have significant difference in swing phase time in Parkinson's patients. The average Hoehn and Yahr scale (H&Y scale) was 4.3 ± 2.5 reported in the study participants. In a 10-week follow-up evaluation, the stance duration was shown to be substantial, as was the slower speed gait in the baseline condition. Excessive flexion was discovered in our investigation at the lower limb joints, particularly the knee and ankle. Patients with Parkinson's disease had similar maximum dorsiflexion and minimum plantarflexion values in stance. The trunk fraction conditions were found significant in patients after rehabilitation training. The best response to rehabilitation treatment was seen when the trunk was rotated. When steps and posture distribution analysis performed, we found that the trunk flexure 1 (p < 0.05), and trunk flexure 2 (p < 0.01) were shown significant values. When GRF threshold characteristics are employed, mean accuracy improves by 52%. Regardless of gait posture, the step regular trunk flexure had significantly higher posture than the corresponding level steps, with a considerable rise in the 50 in trunk dorsiflexion 2 gait relative to the step "L." This study shows that there was some significant improvement observed in the gait parameters among patients with PD's which shows positive impact of the intervention. Furthermore, rehabilitation programmes can aid and improve poor gait features in patients with Parkinson's disease, especially those who are in the early stages of the condition. This gait and balance research provides a rationale for intervention treatments, and their use in clinical practise enhances evidence of therapeutic efficacy. However, prolonged follow-up is needed to determine whether the advantages will remain all across disease's course, and future studies may recommend a specific rehabilitation technique based on gait analysis results.

Corrigendum to "An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis".

[This corrects the article DOI: 10.1155/2021/6712785.].

Association of Lower Extremity Vascular Disease, Coronary Artery, and Carotid Artery Atherosclerosis in Patients with Type 2 Diabetes Mellitus.

The motive of this article is to present the case study of patients to investigate the association between the ultrasonographic findings of lower extremity vascular disease (LEAD) and plaque formation. Secondly, to examine the association between the formation of coronary artery and carotid artery atherosclerosis in patients with type 2 diabetes mellitus. 124 patients with type 2 diabetes (64 males and 60 females with the age group 25-78 years) are considered for the research studies who have registered themselves in the Department of Endocrinology and Metabolism from April 2017 to February 2019. All participants have reported their clinical information regarding diabetes, alcohol consumption, smoking status, and medication. The blood samples from subjects are collected for measurement of HbA1c, total cholesterol, triglycerides, HDL-c, and LDL-c levels. Two-dimensional ultrasound has been used to measure the inner diameter, peak flow velocity, blood flow, and spectral width of the femoral artery, pop artery, anterior iliac artery, posterior tibial artery, and dorsal artery and to calculate the artery stenosis degree. Independent factors of atherosclerosis are determined by multivariate logistic regression analysis. The results are evaluated within the control group and it is found that there is no significant impact of gender, age, and body mass index (P > 0.05) on the lower extremity vascular diseases. Those with smoking, alcohol consumption, hypertension, and dyslipidemia have higher positive rate (P < 0.05). The type 2 diabetes mellitus group has higher diastolic blood pressure and lower triglyceride (P < 0.05). Diastolic blood pressure, HbA1C, total cholesterol, HDL-c, and LDL-C are not remarkably dissimilar between the type 2 diabetes mellitus group and the control group (P > 0.05). Compared with the control group, the type 2 diabetes mellitus group has higher frequency of lower extremity vascular diseases in the dorsal artery than in the pop artery (P < 0.05). The blood flow of type 2 diabetes mellitus group is found to be lower than that of the control group, especially in the dorsal artery (P < 0.05). The blood flow velocity of the dorsal artery is accelerated (P < 0.01). Among 117 patients of type 2 diabetes mellitus (94.35%) with a certain degree of injury, there are 72 cases of type I carotid stenosis (58.06%), 30 cases of type II carotid stenosis (24.19%), and 15 cases of type III carotid stenosis (12.10%). Out of 108 subjects in the control group, there are 84 cases of type 0 carotid stenosis (77.78%), 19 cases of type I carotid stenosis (17.59%), 5 cases of type II carotid stenosis (4.63%), and 0 case of type III carotid stenosis (0.00%). Compared with the control group, carotid stenosis is more common in patients with type 2 diabetes mellitus (P < 0.05). Age, smoking, duration of diseases, systolic blood pressure, and degree of carotid stenosis are found to be associated with atherosclerosis. The findings suggest that the color Doppler ultrasonography can give early warning when applied in patients with carotid and lower extremity vascular diseases to delay the incidence of diabetic macroangiopathy and to control the development of cerebral infarction, thus providing an important basis for clinical diagnosis and treatment.

Smartphone-Based mHealth and Internet of Things for Diabetes Control and Self-Management.

In patients with chronic diseases condition, mobile health monitoring facility proves to play a significant role in providing significant assistance toward personal management. This research examined the use of smartphones by diabetes patients and their intentions to apply them for self-care and monitoring as well as management. This cross-sectional survey-based study was conducted in Jul-Aug 2021 with 200 diabetic patients (especially type 2) who were visiting specialized clinics and hospitals of Gujrat state, India. A validated questionnaire survey was designed to collect data, which included questions about demographics, information pertaining to other, use of cellphones, the Internet, and the intention to implement smartphones for diabetes monitoring, self-care, and self-management. A highest number of studied participants have mobile phone (97.5%) and smartphones (87%) and access the Internet on daily basis (83.5%). Younger participants were more inclined to use smartphone apps and have also shown more interest for continuous use in the future (p < 0.01). The majority of participants used apps for nutritional planning (85.5%), to monitor glucose control (76.5%), and for scheduling of diabetes appointments on the calendar (90.5%). Recommendations to use mobile app by doctors or healthcare profession were reported by 20.5% of the participants and attitude and future intention to use mobile apps were reported by the majority of participants. The majority of type 2 diabetes patients choose to use their cellphones and the internet or mobile phone reminder system for medication as well as to plan their diets, monitor their blood sugar levels, and communicate with their doctors. The findings of this research can be used to develop strategies and implement mHealth-based therapies to assist patients with type 2 diabetes to efficiently manage their health and might contribute to reducing patients' out-of-pocket expenditure as well as reducing disability-adjusted life years (DAILY) attributed by DM.

Aerobics Image Classification Algorithm Based on Modal Symmetry Algorithm.

There exist large numbers of methods/algorithms which can be used for the classification of aerobic images. While the current method is used to classify the aerobics image, it cannot effectively remove the noise in the aerobics image. The classification time is long, and there are problems of poor denoising effect and low classification efficiency. Therefore, the aerobics image classification algorithm based on the modal symmetry algorithm is proposed. The method of nonlocal mean filtering based on structural features is used to denoise the aerobics image, and the pyramid structure of the image is introduced to decompose the aerobics image. According to the denoising and decomposition results, the enhancement of aerobics image is realized by the logarithmic image processing (LIP) model and gradient sharpening method. Finally, the aerobics image after the enhancement is classified by a modal symmetry algorithm. Experimental results show that the proposed method has a good denoising effect and high classification efficiency, which shows that the algorithm has significant effectiveness and high application performance.

The Clinical Value of 3D Ultrasonic Measurement of the Ratio of Gestational Sac Volume to Embryo Volume in IoT-Based Prediction of Pregnancy Outcome.

The objective of the research study is to investigate the use of three-dimensional ultrasonic measurement technology, to determine the size of gestational sac and embryo volume, and to use the ratio of gestational sac volume to embryo volume in IoT-based prediction of pregnancy outcome. The abnormal and normal pregnancy identifiers are there, which assists in prediction of pregnancy outcomes: whether the pregnancy is normal or may suffer pregnancy loss during first trimester. For the observational study, 500 singleton pregnant women who made an appointment for delivery in Qiqihar Hospital from January 2015 to June 2019 were considered. The 500 pregnant women received transvaginal ultrasound at 6+0 ∼ 8+0 weeks of gestational age to measure gestational sac volume (GSV), yolk sac volume (YSV), and germ volume (GV). According to pregnancy outcome, they were divided into fine group (n = 435) and abortion group (n = 65). Among the 500 cases, 435 had normal delivery and 65 had abortions. According to the results of gestational age (GA) analysis, the pregnancy success rates at 6 (n = 268), 7 (n = 184), and 8 weeks (n = 48) were 85.8%, 87.5%, and 91.7%, respectively. Comparison of pregnancy failure rate among the three groups shows statistically significant difference. The morphology of germ, yolk sac, and gestational sac cannot be used as a predictor of pregnancy outcome in various degrees. The results of multivariate Cox proportional regression analysis show the following: the ratio of germ volume (GV) to gestational sac volume (GSV) (P=0.008) has an impact on the prediction of spontaneous abortion prognosis, showing statistically significant difference; yolk sac volume (YSV), germ volume (GV), and gestational sac volume (GSV) have no effect on the prediction of spontaneous abortion prognosis (P > 0.05). The ratio of GSV to germ volume has a strong prognostic value for pregnancy results. To a certain extent, the ratio of gestational sac volume to germ volume can predict spontaneous pregnancy abortion at 6th week of gestation, providing a theoretical basis for clinical ultrasound pregnancy examination indicators.

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis.

Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.