Blockchain-Based Reversible Data Hiding for Securing Medical ImagesRead the full article
Journal of Healthcare Engineering provides a vehicle for the exchange of advanced knowledge, emerging technologies, and innovative ideas related to all aspects of engineering involved in healthcare delivery processes and systems.
Chief Editor, Professor Zollo, has research expertise in neuro-robotics and biomedical technologies for neuroscience, rehabilitation and assistance robotics, and robotic and mechatronic devices for personal assistance and service robotics.
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EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT
Healthcare is one of the most promising domains for the application of Internet of Things- (IoT-) based technologies, where patients can use wearable or implanted medical sensors to measure medical parameters anywhere and anytime. The information collected by IoT devices can then be sent to the health care professionals, and physicians allow having a real-time access to patients’ data. However, besides limited batteries lifetime and computational power, there is spatio-temporal correlation, where unnecessary transmission of these redundant data has a significant impact on reducing energy consumption and reducing battery lifetime. Thus, this paper aims to propose a routing protocol to enhance energy-efficiency, which in turn prolongs the sensor lifetime. The proposed work is based on Energy Efficient Routing Protocol using Dual Prediction Model (EERP-DPM) for Healthcare using IoT, where Dual-Prediction Mechanism is used to reduce data transmission between sensor nodes and medical server if predictions match the readings or if the data are considered critical if it goes beyond the upper/lower limits of defined thresholds. The proposed system was developed and tested using MATLAB software and a hardware platform called “MySignals HW V2.” Both simulation and experimental results confirm that the proposed EERP-DPM protocol has been observed to be extremely successful compared to other existing routing protocols not only in terms of energy consumption and network lifetime but also in terms of guaranteeing reliability, throughput, and end-to-end delay.
Cost-Effectiveness Analysis Based on Intelligent Electronic Medical Arthroscopy for the Treatment of Varus Knee Osteoarthritis
The incidence of inverted knee osteoarthritis is slowly increasing, there are technical limitations in the treatment, and the operation is difficult. In this article, we will study the benefits and costs of arthroscopic cleaning treatments based on intelligent electronic medicine. This article focuses on knee osteoarthritis patients in the EL database. There are 12 male patients, accounting for 66.67%, and 6 female patients, accounting for 33.33%. The average body mass index (BMI) of the patients was 28.08, the average time from first knee discomfort to surgery was 28.44 months, and the average time of arthroscopic debridement treatment for patients with VKOH knee osteoarthritis was 143.11 minutes. One case of perioperative complication occurred within 35 days after operation, which was a soleus muscle intermuscular venous thrombosis. After immobilization and enhanced anticoagulation for 1 week, it was stable without risk of shedding. The average postoperative study time was 20.00 months. The electronic medical arthroscopy cleaning treatment plan in this article can greatly improve the quality of life of patients and can check the pathological state in time, with low cost. In the course of treatment, comprehensive treatment costs can be saved by 45%. Arthroscopic clean-up treatment can not only reduce knee pain and other uncomfortable symptoms, restore normal knee joint function, and improve the quality of life of patients, but also correct the unequal length of the lower limbs, thereby avoiding spinal degeneration caused by knee instability. Therefore, it is the first choice for the treatment of advanced knee osteoarthritis in patients with VKOH.
Pathological Myopia Image Recognition Strategy Based on Data Augmentation and Model Fusion
The automatic diagnosis of various retinal diseases based on fundus images is important in supporting clinical decision-making. Convolutional neural networks (CNNs) have achieved remarkable results in such tasks. However, their high expression ability possibly leads to overfitting. Therefore, data augmentation (DA) techniques have been proposed to prevent overfitting while enriching datasets. Recent CNN architectures with more parameters render traditional DA techniques insufficient. In this study, we proposed a new DA strategy based on multimodal fusion (DAMF) which could integrate the standard DA method, data disrupting method, data mixing method, and autoadjustment method to enhance the image data in the training dataset to create new training images. In addition, we fused the results of the classifier by voting on the basis of DAMF, which further improved the generalization ability of the model. The experimental results showed that the optimal DA mode could be matched to the image dataset through our DA strategy. We evaluated DAMF on the iChallenge-PM dataset. At last, we compared training results between 12 DAMF processed datasets and the original training dataset. Compared with the original dataset, the optimal DAMF achieved an accuracy increase of 2.85% on iChallenge-PM.
Efficient Automated Disease Diagnosis Using Machine Learning Models
Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease may control the death rate due to these diseases. In this paper, an efficient automated disease diagnosis model is designed using the machine learning models. In this paper, we have selected three critical diseases such as coronavirus, heart disease, and diabetes. In the proposed model, the data are entered into an android app, the analysis is then performed in a real-time database using a pretrained machine learning model which was trained on the same dataset and deployed in firebase, and finally, the disease detection result is shown in the android app. Logistic regression is used to carry out computation for prediction. Early detection can help in identifying the risk of coronavirus, heart disease, and diabetes. Comparative analysis indicates that the proposed model can help doctors to give timely medications for treatment.
TCM-ISP: A Comprehensive Intelligent Service Platform for Industry Chain of Traditional Chinese Medicines
In order to promote information interaction, intelligent regulation, and scale management in Chinese medicines industry, in this paper, a Chinese medicines intelligent service platform with characteristics of flexibility, versatility, and individuation was designed under the guidance of theoretical model of intelligent manufacturing of Chinese medicines (TMIM). TCM-ISP is a comprehensive intelligent service platform that can be flexibly applied to all links of Chinese medicines industry chain, which realizes data integration and real-time transmission as well as intelligent-flexible scheduling of equipment in response to different demand. The platform took logical framework of data flow as the core and adopts the modular design in which microcontroller and sensor module are independent to obtain overall design scheme of TCM-ISP that contains the diagram of overall framework, hardware structure, and software technology. Then, on the groundwork of overall design scheme and modern science technology, TCM-ISP was successfully constructed with flexible, intelligent, and networked characteristics in which TTL-USB and TTL-RS485S were utilized to build unified interface between boards with supporting hot-plugging mode. The results of platform tests show that TCM-ISP can not only successfully realize the integration, real-time transmission, and display of data information but also well accomplish remote intelligent-flexible control of equipment and allow flexible configuration and expansion of sensors and devices according to the needs of each link in TCM’s industry chain. It is of great practical significance to the pursuit of intelligent manufacturing of Chinese medicines and the promotion of modernization of Chinese medicines industry.
A Data-Driven Predictive Machine Learning Model for Efficiently Storing Temperature-Sensitive Medical Products, Such as Vaccines: Case Study: Pharmacies in Rwanda
Temperature control is the key element during medicine storage. Pharmacies sell some medical products which are kept in fridges. The opening and closing of the fridge while taking some medicine makes the outside hot air enter the fridge, which will increase the inner fridge temperature. When the frequency of opening and closing of the fridge is increased, the temperature may go beyond the allowed storage temperature range. In this paper, we are proposing a model with the help of machine learning that will be used in multiple chambers fridges to keep indicating the time remaining for the inner temperature to go beyond the allowed range, and if the time is short, the system will propose to the pharmacist not to open that particular room and proposes a room that has enough time slots (time to reach the upper limit temperature). By using training data got from a thermoelectric cooler-based fridge, we constructed a multiple linear regression model that can predict the required time for a given room to reach the cut-off temperature in case that room is opened. The built model was evaluated using the coefficient of determination R2 and is found to be 77%, and then it can be used to develop a multiple room smart fridge for efficiently storing highly sensitive medical products.