Two-Stage Segmentation Framework Based on Distance Transformation

With the rise of deep learning, using deep learning to segment lesions and assist in diagnosis has become an effective means to promote clinical medical analysis. However, the partial volume effect of organ tissues leads to unclear and blurred edges of ROI in medical images, making it challenging to achieve high-accuracy segmentation of lesions or organs. In this paper, we assume that the distance map obtained by performing distance transformation on the ROI edge can be used as a weight map to make the network pay more attention to the learning of the ROI edge region. To this end, we design a novel framework to flexibly embed the distance map into the two-stage network to improve left atrium MRI segmentation performance. Furthermore, a series of distance map generation methods are proposed and studied to reasonably explore how to express the weight of assisting network learning. We conduct thorough experiments to verify the effectiveness of the proposed segmentation framework, and experimental results demonstrate that our hypothesis is feasible.

IoT network security using autoencoder deep neural network and channel access algorithm

Internet-of-Things (IoT) creates a significant impact in spectrum sensing, information retrieval, medical analysis, traffic management, etc. These applications require continuous information to perform a specific task. At the time, various intermediate attacks such as jamming, priority violation attacks, and spectrum poisoning attacks affect communication because of the open nature of wireless communication. These attacks create security and privacy issues while making data communication. Therefore, a new method autoencoder deep neural network (AENN) is developed by considering exploratory, evasion, causative, and priority violation attack. The created method classifies the transmission outcomes used to predict the transmission state, whether it is jam data transmission or sensing data. After that, the sensing data is applied for network training that predicts the intermediate attacks. In addition to this, the channel access algorithm is used to validate the channel for every access that minimizes unauthorized access. After validating the channel according to the neural network, data have been transmitted over the network. The defined process is implemented, and the system minimizes different attacks on various levels of energy consumption. The effectiveness of the system is implemented using TensorFlow, and the system ensures the 99.02% of detection rate when compared with other techniques.

Methods of forensic medical analysis of acute intoxications with ethyl alcohol (review of literary sources)

The article presents an overview of current literature on the problem of forensic diagnosis of acute poisoning by ethanol and its surrogates. Morphological and forensic chemical criteria for the diagnosis of ethanol intoxication are presented, variants of patho- and thanatogenesis are described, as well as modern methods for diagnosing these poisonings. Aim of the work. Review of current literature data on the problem of forensic diagnosis of acute poisoning by ethanol and its surrogates. Conclusion. In our opinion, it is necessary to introduce into the practical work of the bureau of forensic examinations new promising technologies for the diagnosis of acute intoxication with ethyl alcohol and its surrogates.

Smart Medical Prediction for Guidance: A Mechanism Study of Machine Learning

Data analysis and prediction have gradually attracted more and more attention in the smart healthcare industry. The smart medical prediction system is of great importance to the enterprise strategy and business development, and it is also of great value to provide medical advices for patients and assist patient guidance. The research theme is the use of machine learning technologies with the application in the areas of smart medical analysis. In this paper, the actual data of the smart medical industry were statistically analysed and visualized according to the features, and the most influential feature combinations were selected for the establishment of the prediction model. Based on machine learning technology, namely, random forest, the guidance prediction model is established, and the combination of features is repeatedly adjusted to improve its accuracy. The practical significance of this paper is to provide a high-precision solution for smart medical data analysis and to realize the proposed data analysis and prediction on the cloud platform based on the Spark environment.

Historical and medical aspects of the formation and development of regional general medical practice

The results of the historical and medical analysis of the processes of formation and development of primary care in the Orenburg Region are quite relevant, especially in connection with the renewed demand for resolving urgent issues that have arisen in the system of domestic health care. Consideration of the above aspects on the example of a separate territory, the Orenburg Region, is significant, given that public health care is formed by various structures of territorial systems in the context of demographic, social and economic gradations.

Heat Sterilization Effects on Polymeric, FDM-Optimized Orthopedic Cutting Guide for Surgical Procedures

Improvements in software for image analysis have enabled advances in both medical and engineering industries, including the use of medical analysis tools to recreate internal parts of the human body accurately. A research analysis found that FDM-sourced elements have shown viability for a customized and reliable approach in the orthopedics field. Three-dimensional printing has allowed enhanced accuracy of preoperative planning, leading to reduced surgery times, fewer unnecessary tissue perforations, and fewer healing complications. Furthermore, using custom tools chosen for each procedure has shown the best results. Bone correction-related surgeries require customized cutting guides for a greater outcome. This study aims to assess the biopolymer-based tools for surgical operations and their ability to sustain a regular heat-sterilization cycle without compromising the geometry and fit characteristics for a proper procedure. To achieve this, a DICOM and FDM methodology is proposed for fast prototyping of the cutting guide by means of 3D engineering. A sterilization test was performed on HTPLA, PLA, and nylon polymers. As a result, the unique characteristics within the regular autoclave sterilization process allowed regular supplied PLA to show there were no significant deformations, whilst annealed HTPLA proved this material’s capability of sustaining repeated heat cycles due to its crystallization properties. Both of these proved that the sterilization procedures do not compromise the reliability of the part, nor the safety of the procedure. Therefore, prototypes made with a similar process as this proposal could be safely used in actual surgery practices, while nylon performed poorly because of its hygroscopic properties.

Fluid Control with Hydrophobic Pillars in Paper-Based Microfluidics

Paper-based microfluidics has been widely used in chemical and medical analysis applications. In the conventional paper-based microfluidic approach, fluid is propagating inside the porous structure, and the flow direction of the fluid propagation is usually controlled with the pre-defined hydrophobic barrier (e.g. wax). However, the fluid propagation velocity inside the paper-based microfluidic devices largely depends on the material properties of paper and fluid, the relative control method is rarely reported. In this study, a fluid propagation velocity control method is proposed for paper-based microfluidics: hydrophobic pillar arrays with different configurations were deposited in the microchannels in paper-based microfluidics for flow speed control, result indicates the deposited hydrophobic pillar arrays can effectively slow down the fluid propagation at different levels and can be used to passively control the fluid propagation inside microchannels for paper-based microfluidics. For the demonstration of the proposed fluid control methods, a paper-based microfluidic device for nitrite test in water was also fabricated. The proposed fluid control method for paper-based microfluidics may have significant importance for applications that involve sequenced reactions and more actuate fluid manipulation.

Medical Analysis for Surgeryon Patients Complaining of Sciatic Pain

A prospective researchwas performed on 50 patients submitted for radiological screening in Samarra general hospital complaining of chronic backache and sciatica. The study was completed for patients recommended by senior neurosurgeons, orthopedicians, &rheumatologist, to the radiological unit in Samarra public hospital from October 2016 to October 2017. Radiological studies were analyzed by senior radiologists of Samarra public hospital in conjunction with the recommending senior doctors. Patients who are operated on were all proven to have chronic Lateral Recess Syndrome both radiological and surgically.