Deep Learner System Based on Focal Color Retinal Fundus Images to Assist in Diagnosis

Retinal diseases are a serious and widespread ophthalmic disease that seriously affects patients' vision and quality of life. With the aging of the population and the change in lifestyle, the incidence rate of retinal diseases has increased year by year. However, traditional diagnostic methods often require experienced doctors to analyze and judge fundus images, which carries the risk of subjectivity and misdiagnosis. This paper will analyze an intelligent medical system based on focal retinal image-aided diagnosis and use a convolutional neural network (CNN) to recognize, classify, and detect hard exudates (HEs) in fundus images (FIs). The research results indicate that under the same other conditions, the accuracy, recall, and precision of the system in diagnosing five types of patients with pathological changes under color retinal FIs range from 86.4% to 98.6%. Under conventional retinopathy FIs, the accuracy, recall, and accuracy of the system in diagnosing five types of patients ranged from 70.1% to 85%. The results show that the application of focus color retinal FIs in the intelligent medical system has high accuracy and reliability for the early detection and diagnosis of diabetic retinopathy and has important clinical applications.

Reliability analysis of body sensor networks with correlated isolation groups

Body sensor networks (BSNs) are playing a crucial role in tackling arising challenges during the COVID-19 pandemic. This work contributes by modeling and analyzing the BSN reliability considering the effects of correlated functional dependence (FDEP) and random isolation time behavior. Particularly, the FDEP exists in BSNs where a relay is utilized to assist the communication between some biosensors and the sink device. When the relay malfunctions, the dependent biosensors may communicate directly with the sink for a limited, uncertain time. These biosensors then become isolated from the rest of the BSN when their remaining power depletes to the level insufficient to support the direct communication. Moreover, multiple biosensors sharing the same relay and a biosensor communicating with the sink via several alternative relays create correlations among different FDEP groups. In addition, the competition in the time domain exists between the local failure of the relay and the propagated failures of dependent biosensors. Both the correlation and competition complicate the reliability modeling and analysis of BSNs. This work proposes a combinatorial and analytical methodology to address both effects in the BSN reliability analysis. The proposed method is demonstrated using a detailed case study and verified using a continuous-time Markov chain method.

Advanced and Smart Textiles during and after the COVID-19 Pandemic: Issues, Challenges, and Innovations

The COVID-19 pandemic has hugely affected the textile and apparel industry. Besides the negative impact due to supply chain disruptions, drop in demand, liquidity problems, and overstocking, this pandemic was found to be a window of opportunity since it accelerated the ongoing digitalization trends and the use of functional materials in the textile industry. This review paper covers the development of smart and advanced textiles that emerged as a response to the outbreak of SARS-CoV-2. We extensively cover the advancements in developing smart textiles that enable monitoring and sensing through electrospun nanofibers and nanogenerators. Additionally, we focus on improving medical textiles mainly through enhanced antiviral capabilities, which play a crucial role in pandemic prevention, protection, and control. We summarize the challenges that arise from personal protective equipment (PPE) disposal and finally give an overview of new smart textile-based products that emerged in the markets related to the control and spread reduction of SARS-CoV-2.

A Survey of Authentication in Internet of Things-Enabled Healthcare Systems

The Internet of medical things (IoMT) provides an ecosystem in which to connect humans, devices, sensors, and systems and improve healthcare services through modern technologies. The IoMT has been around for quite some time, and many architectures/systems have been proposed to exploit its true potential. Healthcare through the Internet of things (IoT) is envisioned to be efficient, accessible, and secure in all possible ways. Even though the personalized health service through IoT is not limited to time or location, many associated challenges have emerged at an exponential pace. With the rapid shift toward IoT-enabled healthcare systems, there is an extensive need to examine possible threats and propose countermeasures. Authentication is one of the key processes in a system's security, where an individual, device, or another system is validated for its identity. This survey explores authentication techniques proposed for IoT-enabled healthcare systems. The exploration of the literature is categorized with respect to the technology deployment region, as in cloud, fog, and edge. A taxonomy of attacks, comprehensive analysis, and comparison of existing authentication techniques opens up possible future directions and paves the road ahead.

A Review on Data-Driven Quality Prediction in the Production Process with Machine Learning for Industry 4.0

The quality-control process in manufacturing must ensure the product is free of defects and performs according to the customer’s expectations. Maintaining the quality of a firm’s products at the highest level is very important for keeping an edge over the competition. To maintain and enhance the quality of their products, manufacturers invest a lot of resources in quality control and quality assurance. During the assembly line, parts will arrive at a constant interval for assembly. The quality criteria must first be met before the parts are sent to the assembly line where the parts and subparts are assembled to get the final product. Once the product has been assembled, it is again inspected and tested before it is delivered to the customer. Because manufacturers are mostly focused on visual quality inspection, there can be bottlenecks before and after assembly. The manufacturer may suffer a loss if the assembly line is slowed down by this bottleneck. To improve quality, state-of-the-art sensors are being used to replace visual inspections and machine learning is used to help determine which part will fail. Using machine learning techniques, a review of quality assessment in various production processes is presented, along with a summary of the four industrial revolutions that have occurred in manufacturing, highlighting the need to detect anomalies in assembly lines, the need to detect the features of the assembly line, the use of machine learning algorithms in manufacturing, the research challenges, the computing paradigms, and the use of state-of-the-art sensors in Industry 4.0.

Evaluating the sustainability of smart technology applications in healthcare after the COVID-19 pandemic: A hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence

During the COVID-19 pandemic, some smart technology applications were more effective than had been expected, whereas some others did not achieve satisfactory performance. Consequently, whether smart technology applications in healthcare are sustainable is a question that warrants investigation. To address this question, a hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence was proposed in this study and then used to evaluate the sustainability of smart technology applications in healthcare. The contribution of this research is its subjective evaluation of the sustainability of smart technology applications followed by correction of the evaluation outcome on the basis of the applications' objective performance during the COVID-19 pandemic. To this end, a fuzzy nonlinear programming model was formulated and optimised. In addition, the impact of several major global events that occurred during the pandemic on the sustainability of smart technology applications was considered. The proposed methodology was applied to evaluate the sustainability levels of eight smart technology applications in healthcare. According to the experimental results, three applications-namely healthcare apps, smartwatches, and remote temperature scanners-are expected to be highly sustainable in healthcare, whereas one application, namely smart clothing, is not.

Optimizing and evaluating PCR-based pooled screening during COVID-19 pandemics

Population screening played a substantial role in safely reopening the economy and avoiding new outbreaks of COVID-19. PCR-based pooled screening makes it possible to test the population with limited resources by pooling multiple individual samples. Our study compared different population-wide screening methods as transmission-mitigating interventions, including pooled PCR, individual PCR, and antigen screening. Incorporating testing-isolation process and individual-level viral load trajectories into an epidemic model, we further studied the impacts of testing-isolation on test sensitivities. Results show that the testing-isolation process could maintain a stable test sensitivity during the outbreak by removing most infected individuals, especially during the epidemic decline. Moreover, we compared the efficiency, accuracy, and cost of different screening methods during the pandemic. Our results show that PCR-based pooled screening is cost-effective in reversing the pandemic at low prevalence. When the prevalence is high, PCR-based pooled screening may not stop the outbreak. In contrast, antigen screening with sufficient frequency could reverse the epidemic, despite the high cost and the large numbers of false positives in the screening process.

Recommending Suitable Smart Technology Applications to Support Mobile Healthcare after the COVID-19 Pandemic Using a Fuzzy Approach

The COVID-19 pandemic seems to be entering its final stage. However, to restore normal life, the applications of smart technologies are still necessary. Therefore, this research is dedicated to exploring the applications of smart technologies that can support mobile healthcare after the COVID-19 pandemic. To this end, this study compares smart technology applications to support mobile healthcare within the COVID-19 pandemic with those before the pandemic, so as to estimate possible developments in this field. In addition, to quantitatively assess and compare smart technology applications that may support mobile healthcare after the COVID-19 pandemic, the calibrated fuzzy geometric mean (CFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) approach is applied. The proposed methodology has been applied to evaluate and compare nine potential smart technology applications for supporting mobile healthcare after the COVID-19 pandemic. According to the experimental results, "vaccine passport and related applications" and "smart watches" were the most suitable smart technology applications for supporting mobile healthcare after the COVID-19 pandemic.

Advancements in Biosensor Technologies for Medical Field and COVID-19 Pandemic

World health organization (WHO) has declared the COVID-19 outbreak as a public health emergency of international concern and then as a pandemic on 30th of January and 11th of March 2020, respectively. After such concern, the world scientific communities have rushed to search for solutions to bring down the disease’s spread, fast-paced vaccine development, and associated medical research using modern technologies. Biosensor technologies play a crucial role in diagnosing various medical diseases, including COVID-19. The present paper describes the major advancement of biosensor-based technological solutions for medical diagnosis, including COVID-19. This review-based work covers the biosensors and their working principles in the context of medical applications. The paper also discusses different biosensors and their applications to tackle medical issues, including this ongoing pandemic.

Trustworthy Wireless Sensor Networks for Monitoring Humidity and Moisture Environments

Wireless sensors networks (WSNs) are characterized by flexibility and scalability in any environment. These networks are increasingly used in agricultural and industrial environments and have a dual role in data collection from sensors and transmission to a monitoring system, as well as enabling the management of the monitored environment. Environment management depends on trust in the data collected from the surrounding environment, including the time of data creation. This paper proposes a trust model for monitoring humidity and moisture in agricultural and industrial environments. The proposed model uses a digital signature and public key infrastructure (PKI) to establish trust in the data source, i.e., the trust in the sensor. Trust in data generation is essential for real-time environmental monitoring and subsequent analyzes, thus timestamp technology is implemented here to further ensure that gathered data are not created or changed after the assigned time. Model validation is performed using the Castalia network simulator by testing energy consumption at the receiver and sender nodes and the delay incurred by creating or validating a trust token. In addition, validation is also performed using the Ascertia TSA Crusher application for the time consumed to obtain a timestamp from the free TSA. The results show that by applying different digital signs and timestamps, the trust entity of the WSN improved significantly with an increase in power consumption of the sender node by up to 9.3% and receiver node by up to 126.3% for a higher number of nodes, along with a packet delay of up to 15.6% and an average total time consumed up to 1.186 s to obtain the timestamp from the best chosen TSA, which was as expected.

Multicast Traffic Throughput Maximization through Joint Dynamic Modulation and Coding Schemes Assignment, and Transmission Power Control in Wireless Sensor Networks

The paper concerns multicast packet traffic throughput maximization in multi-hop wireless sensor networks with time division multiple access to radio channel. We assume that the modulation and coding schemes (MCSs) that are used by the (broadcasting) nodes as well as the transmission power of the nodes are adjustable. This leads to the main research question studied in this paper: to what extent traffic throughput can be increased by proper MCSs assignment and transmission power control (TPC) at the nodes? To answer this question, we introduce mixed-integer programming formulations for joint MCSs assignment and TPC optimization, together with a solution algorithm. Finally, we present a numerical study illustrating the considerations of the paper. The numerical results show a significant gain being achieved by proper MCSs assignment, which is further increased by applying TPC.

Technological Resources for Fighting COVID-19 Pandemic Health Issues

The COVID-19 pandemic has had a global effect with several people dying daily due to the dreaded disease. Therefore, each individual has a duty to support the efforts of their countries either financially, socially, technically, or by any other means to contribute to the fight against the COVID-19 pandemic. During this crisis, engineers can come up with innovations to fight the pandemic. One of the reasons for the death of a patient suffering from COVID-19 was the lack of resources required for patient care. The doctors who are taking care of COVID patients could get infected due to lack or the deficiency of available safety kits. Some of the resources required to fight COVID-19 are personal protective equipment (PPE) (e.g. gloves, gowns, face masks and shields for respiratory and eye protection respectively), mechanical ventilators and body vital monitoring devices. Engineers can contribute to the fight against COVID-19, by developing compact size ventilators, 3D printed face shield, masks, door handles, hand sanitizer, etc. The available medicines to fight the disease are still under development and trials with limited options at present has led to deaths among patients mostly those who are elderly or having any co-morbid condition. The deficiency of medicine availability can be resolved up to some extent by applying tools of supply chain management.

Tissue Engineering and its Significance in Healthcare During COVID-19 Pandemic: Potential Applications and Perspectives

In the present times of the COVID-19 pandemic, there is a great need for new therapeutic and diagnostic strategies to prevent infectious diseases worldwide. Tissue engineering covers the phenomenon of the evolution of tissue, its behavior and growth factors that are better supported in the medical environment. This area of tissue engineering can support the treatment of infected patients of COVID-19 and can help fight the current crisis and viral outbreaks in general. This study aims to identify the significant advancement of tissue engineering for taking up the challenges posed by COVID-19. Major challenges faced during the COVID-19 pandemic situation in the current scenario are discussed. The significant advancements of tissue engineering in the medical field are listed in chronological order. The positive impacts of tissue engineering during the COVID 19 crisis are discussed and finally its useful applications during the ongoing COVID-19 pandemic situation are identified and briefed. This branch of science’s primary importance is to provide biological alternatives that can perform full or partial functions of the damaged, malfunctioned and failing organs or tissues in humans. It is helpful for the supply of convalescent plasma to patients especially during COVID-19. A donor is selected strictly based on a validated case of COVID-19 contagion. The donor must confirm a negative follow-up molecular examination, free from manifestations; usual good health and other pre-donation screening procedures are to be followed.

Bioengineering Technology in Context of COVID-19 Pandemic: Potential Roles and Applications

Bioengineering (BE) technology has significant influence on the healthcare environment. This has grown steadily particularly since the medical practice has become more technology based. We have tried to assess the impact of bioengineering in tackling the COVID-19 pandemic. The use of bioengineering principles in healthcare has been evaluated. The practical implications of these technologies in fighting the current global health pandemic have been presented. There has been a shared drive worldwide to harness the advancements of bioengineering to combat COVID-19. These efforts have ranged from small groups of volunteers to large scale research and mass production. Together the engineering and medical fields have worked to address areas of critical need including the production and delivery of personal protective equipment, ventilators as well as the creation of a viable vaccine. The fight against COVID-19 has helped highlight the work and contributions of so many professionals in the bioengineering fields who are working tirelessly to help our health services cope. Their innovation and ingenuity are paving the way to successfully beat this virus. We must continue to support these fields as we evolve our health systems to deal with the challenges of healthcare in the future.

Nanomedicine Technology and COVID-19 Outbreak: Applications and Challenges

The coronavirus (COVID-19) pandemic is one of the biggest challenges in the field of healthcare. Nanomedicine is a developing area that has the potential to treat various diseases and control infections. Now, its applications are open for the treatment of COVID-19. We have studied relevant papers through Scopus, Google Scholar, Science Direct and ResearchGate on nanomedicine in context of COVID-19. This paper provides detailed information about nanomedicine in the context of healthcare. It further identifies the primary challenges faced in the current situation. This study provides details about the advancements in the area of nanomedicine in healthcare for fighting the COVID-19 pandemic. Finally, we have identified and discussed various significant applications of nanomedicine in solving challenges thrown by the COVID-19 pandemic. Researchers can work on developing applications of nanoparticles with the size of the novel Coronavirus. Nanomedicine is helpful to repair the cells of an infected patient the help of repair proteins. It also plays a vital role in testing medicine and helps many clinical trials get approval from healthcare agencies. In the future, nanomedicine will be helpful for fighting against this pandemic and creating advancements in healthcare.

Exploring the Impact of COVID-19 Pandemic on Medical Supply Chain Disruption

The COVID-19 outbreak is a worldwide disaster that has disrupted several aspects of human life and its community living. This pandemic has also affected the existing supply chain and the way it was being managed. Therefore, this study’s primary objective is to identify and discuss the significant impact of COVID-19 on the current supply chain, with specific reference to the medical supply chain. This article then recommends the potential solution measures that can reduce the impact of COVID-19 on the existing supply chain during and after the COVID-19 pandemic. In order to do so, we have identified and discussed various significant impacts of COVID-19 on the supply chain. Identifying the impacts helps the policy planner formulate the policies to mitigate these impacts and recover the supply chain at their functional level. Further, this study also suggests possible solution measures that can be adopted to reduce the disruption of the supply chain. These solution measures help management develop the action plan for early recovery from the COVID-19 disruption.

Symbiotic Relationship Between Machine Learning and Industry 4.0: A Review

Industry 4.0 though launched less than a decade ago, has revolutionized the way technologies are being used. It has found its application in almost every field of manufacturing, cybersecurity, health, banking, and other services. Industry 4.0 is heavily dependent on interconnectivity and data. Machine learning (ML) acts as a foundation for building industry 4.0 applications. In this paper, we have provided a broad view of how ML is necessary to accomplish the benefits of industry 4.0. The paper includes ML usage in companies and the limitations of ML, which need to be mitigated. There are also some instances of the failure of ML algorithms and their repercussions. Though industry 4.0 requires a lot more inputs and capital than normal processes, the long-run benefits outweigh the initial costs. ML is gaining popularity, and extensive research is happening to exploit its potential and develop full smart applications.

Virtual Reality (VR) Applications in Cardiology: A Review

Virtual reality (VR) has applications in cardiology to create enhancement, thereby improving the quality of associated planning, treatment and surgery. The need is to study different applications of this technology in the field of cardiology. We have studied research papers on VR and its applications in cardiology through a detailed bibliometric analysis. The study identified five significant steps for proper implementation of this technology in cardiology. Some challenges are to be undertaken by using this technology, and they can provide some benefits; thus, authors contemplate extensive research and development. This study also identifies 10 major VR technology applications in cardiology and provided a brief description. This innovative technology helps a heart surgeon to perform complex heart surgery effectively. Thus, VR applications have the potential for improving decision-making, which helps save human life. VR plays a significant role in the development of a surgical procedure. This technology undertakes 3D heart model information in full colour, which helps to analyze the overall heart vane, blockage and blood flow. With the help of this digital technology, a surgeon can improve the accuracy of heart surgery, and he can simulate the surgery. A surgeon can undertake surgery in a virtual environment on a virtual patient. The unique purpose of this technology is to practice pre-operatively on the specific circumstance. A cardiologist can also check the proper status of inner and outer heart wall layer. Thus, by using this 3D information, the surgeon can now interact with heart data/information without any physical touch. This technology opens a new opportunity to improve the heart surgery and development in cardiovascular treatment to improve patient outcome.

IoT-based GPS assisted surveillance system with inter-WBAN geographic routing for pandemic situations

BACKGROUND

Worldwide pandemic situations drive countries into high healthcare costs and dangerous conditions. Hospital occupancy rates and medical expenses increase dramatically. Real-time remote health monitoring and surveillance systems with IoT assisted eHealth equipment play important roles in such pandemic situations. To prevent the spread of a pandemic is as crucial as treating the infected patients. The COVID-19 pandemic is the ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

METHODS

We propose a surveillance system especially for coronavirus pandemic with IoT applications and an inter-WBAN geographic routing algorithm. In this study, coronavirus symptoms such as respiration rate, body temperature, blood pressure, oxygen saturation, heart rate can be monitored and the social distance with 'mask-wearing status' of persons can be displayed with proposed IoT software (Node-RED, InfluxDB, and Grafana).

RESULTS

The geographic routing algorithm is compared with AODV in outdoor areas according to delivery ratio, delay for priority node, packet loss ratio and bit error rate. The results obtained showed that the geographic routing algorithm is more successful for the proposed architecture.

CONCLUSION

The results show that the use of WBAN technology, geographic routing algorithm, and IoT applications helps to achieve a realistic and meaningful surveillance system with better statistical data.

Significant Applications of Big Data in Industry 4.0

Industry 4.0 is being implemented with the help of advanced technologies. Big data, Artificial Intelligence (AI), Robotics, Internet of Things (IoT), Cloud computing, and 3D printing are the major technologies used to adopt Industry 4.0 successfully. Here, the study’s need is to discuss the major potential of big data for Industry 4.0. These technologies’ primary purpose is to collect the right data to solve the relevant issue during manufacturing and other required services. This technology plays a significant role in creating advancements in this fourth industrial revolution. Conclusively, big data applications are useful for in-process management and productivity improvement in the automation sector. Complex systems of drivers and intelligent sensors can be easily optimized based on information collected using this technology. Big data is the key to gain a competitive leap by reconnoitring the fundamental issues like deviations during the process, quality discriminations, and energy efficiency squander in a manufacturing process. The study discusses the significant applications of big data in Industry 4.0. For a proper surveillance system, industries need to have an immensely technical or personalized way, making big data a valuable source for predicting analysis and operation management based on market insight statistics or information. In upcoming days, big data will provide further advancement in Industry 4.0 and is supposed to play an efficient role in its successful adoption.

Cloud Computing in Solving Problems of COVID-19 Pandemic

Cloud computing facilitates collaboration, communication, and essential online services during the COVID-19 crisis. The current situation of the COVID-19 pandemic has compelled people to work from their homes, but they have to communicate, collaborate online. Thus, we see an essential role of cloud computing in taking up this challenge of working from home and delivering efficiently. A brief review of Cloud Computing service in the context of COVID-19 pandemic is done using recent papers’ by searching keywords such as “Cloud Computing” and “COVID-19” from PubMed’s database SCOPUS and Google Scholar. During the lockdown situation, cloud computing technology helps provide commendable service in the healthcare domain. It provides an advanced infrastructure for facilitating digital transformation. A brief discussion has been made on how cloud computing components are vital for overcoming the ongoing situation. This paper also studies the remote working of cloud computing for the COVID-19 pandemic and finally identified significant cloud computing applications for the COVID-19 pandemic. All countries focus on reducing this virus’s spread, so this technology helps minimize the spread of this virus by providing online services. It provides an innovative environment that enhances the creativity and productivity of healthcare workers. This technology is efficient in detecting, tracking, and monitoring newly infected patients. In the future, this technology will insight and provide control over this infection to save millions of lives worldwide. This technology is also quite helpful to forecast the future impact of the SARS-Co-2 virus.

Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections

Remote symptom tracking is critical for the prevention of Covid-19 spread. The qualified medical staff working in the call centers of primary health care units have to take critical decisions often based on vague information about the patient condition. The congestion and the medical protocols that are constantly changing often lead to incorrect decisions. The proposed platform allows the remote assessment of symptoms and can be useful for patients, health institutes and researchers. It consists of mobile desktop applications and medical sensors connected to cloud infrastructure. The unique features offered by the proposed solution are: (a) dynamic adaptation of Medical Protocols (MP) is supported (for the definition of alert rules, sensor sampling strategy and questionnaire structure) covering different medical cases (pre- or post-hospitalization, vulnerable population, etc.), (b) anonymous medical data can be statistically processed in the context of the research about an infection such as Covid-19, (c) reliable diagnosis is supported since several factors are taken into consideration, (d) the platform can be used to drastically reduce the congestion in various healthcare units. For the demonstration of (b), new classification methods based on similarity metrics have been tested for cough sound classification with an accuracy in the order of 90%.

Dentistry 4.0 technologies applications for dentistry during COVID-19 pandemic

The term, Dentistry 4.0, coincides with Industry 4.0, in which the traditional methods of manufacturing and information are made more precise to enhance process efficiency by using automation and advanced computer technologies. The main of this paper is to discuss the major potential of Dentistry 4.0 technologies in the field of dentistry during Coronavirus (COVID-19) Pandemic. Thereon, Dentistry 4.0 is advancing on its way with the use of advanced technologies in dentistry. Dental healthcare makes an essential part of the overall health of the masses. New technological advancements are essential to make the dentist work quicker, patient comfortable, and process reliable. So, we introduced the concept of Dentistry 4.0 to improve efficiency and impart innovation in dentistry during this pandemic. This paper briefs about the Dentistry 4.0 technologies helpful for the COVID-19 pandemic. Further discusses various issues and challenges in implementing Dentistry 4.0 for dentistry during the COVID-19 pandemic. Finally, the paper identifies and discussed fifteen significant applications of Dentistry 4.0 technologies for dentistry during the COVID-19 pandemic. With the onset of the pandemic, globally, the healthcare sector is taking initiatives to strengthen affordable and high-speed data connectivity. This up-gradation and investment will also help dentists to access patients' data from smaller towns or villages using Dentistry 4.0 technologies. Thus, globally there is the onset of the fourth dentistry revolution, and we understand that this will change the trend of dentistry during and post-COVID-19 Pandemic. Dentistry 4.0 technologies are helpful during the COVID-19 pandemic to create teledentistry, virtual clinical practice and connect all dental devices to improve health conditions. This approach is to help progress towards the integrated capabilities, patient-centric remedies with predicted results in an easier way than the traditional way of the health care industry.