Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction

Fouad, H.; Hassanein, Azza S.; Soliman, Ahmed M.; Al-Feel, Haytham

doi:10.1016/j.measurement.2020.107757

Cited by 91 publications

(39 citation statements)

References 26 publications

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“…[ 135 ] It is estimated that IoT and artificial intelligence (AI) will have huge economic impacts by 2030 and will be increasingly in demand in the coming “postcorona society.” For example, IoT‐connected biosensors with AI may become ubiquitous. [ 136,137 ] Along similar lines, Figure a shows a proposed mobile health system. This system can be used for patient health monitoring with a constant recording and feedback approach, but it can also be extended to track and trace infections for public health protection.…”

Section: Future Outlookmentioning

confidence: 99%

A Review of Piezoelectric and Magnetostrictive Biosensor Materials for Detection of COVID‐19 and Other Viruses

et al. 2020

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fever with or without chills, chest tightness, dry cough, and shortness of breath, while developing patchy to diffuse infiltration of the lungs, as shown radiographically in Figure 1. Identifying and monitoring SARS-CoV-2 infection has become crucially important. A recent projection of the transmission dynamics of SARS-CoV-2 [2] showed that longitudinal serological studies are desperately needed to determine the extent and duration of immunity to SARS-CoV-2. Even in the event of apparent elimination, maintenance of SARS-CoV-2 surveillance is still needed because of a possible resurgence of contagion. In the past, notable viruses have emerged suddenly from obscurity or anonymity, provoking concern from the point of view of immunology regarding their sustained epidemic transmission in naive human populations. More than 70% of these infections have been zoonotic, entering either directly from wild animal reservoirs or indirectly via an intermediate domestic animal host. [3] Ebola virus, avian influenza, human immunodeficiency virus (HIV), and SARS are all examples of zoonoses that have emerged from wild animals, presenting an increasingly serious threat to human health and economies worldwide. Figure 2 shows the trend in the number of people infected with SARS-CoV-2. [4] The spread of the severe acute respiratory syndrome coronavirus has changed the lives of people around the world with a huge impact on economies and societies. The development of wearable sensors that can continuously monitor the environment for viruses may become an important research area. Here, the state of the art of research on biosensor materials for virus detection is reviewed. A general description of the principles for virus detection is included, along with a critique of the experimental work dedicated to various virus sensors, and a summary of their detection limitations. The piezoelectric sensors used for the detection of human papilloma, vaccinia, dengue, Ebola, influenza A, human immunodeficiency, and hepatitis B viruses are examined in the first section; then the second part deals with magnetostrictive sensors for the detection of bacterial spores, proteins, and classical swine fever. In addition, progress related to early detection of COVID-19 (coronavirus disease 2019) is discussed in the final section, where remaining challenges in the field are also identified. It is believed that this review will guide material researchers in their future work of developing smart biosensors, which can further improve detection sensitivity in monitoring currently known and future virus threats.

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Section: Future Outlookmentioning

confidence: 99%

A Review of Piezoelectric and Magnetostrictive Biosensor Materials for Detection of COVID‐19 and Other Viruses

et al. 2020

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“…In a set of 27 cases, the detection outcomes were analyzed with a free reaction recipient operating characteristic (FROC). Further Heuristic Hock Transformation and Artificial intelligence technique has been carried out in the vertebral research on Internet of Medical Things (IoMT) platform which has been shown in the [2,31] To overcome these issues, an ATS-CDM model for vertebral tumor detection and accurate segmentation is proposed in this paper. The feature classification, training, patch labeling, and patch extraction are analyzed mathematically based on the extracted features from the datasets to detect the relative label distances for every voxel.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed ATS-CDM has 98.7% bending and 98.88 % segmentation accuracy which are the best results compared to other methods. The accuracy[31,32] can be calculated as follows: Accuracy = ∑ True (Positive & negative) ∑ True (Positive & negative) & False (Positive & negative)(26)…”

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confidence: 99%

Vertebral Tumor Detection and Segmentation Using Analytical Transform Assisted Statistical Characteristic Decomposition Model

et al. 2020

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Recently, vertebral tumor prediction using image-based vertebral bio-mathematical modeling with accurate segmentation has been considered as a significant area of research. Further, precise lumbar spinal segmentation is essential to the clinicians for tumor analysis. Therefore, precise and reliable segmentation process is required to help radiologists and doctors to identify different vertebrae tumors with better prediction ratio. The exact vertebral disks segmentation of spinal bones from medical images is a complex process in dealing with different deformities and pathologies in accordance to the conventional techniques such as Deep Convolutional Neural Network (DCNN), Finite Element Analysis (FEA), Principal Component and Factor Analysis (PCFA), Multi-Parameter Ensemble Learning (MPEL), Hierarchical Conditional Random Forest (HCRF), and Deep Siamese Neural Network (DSNN). Therefore, to overcome the present drawbacks, Analytical Transform Assisted Statistical Characteristic Decomposition Model (ATS-CDM) is proposed in this paper for the accurate prediction of vertebral tumor detection and segmentation. This technique is used for the calibration of the segmentation procedures in vertebral tumor image prediction and Receiver Operating Curve (ROC) grading for the lumbar spines. The significant objective of the model-fitting algorithm iterates the tumor regions and measures the current region's variance for the accurate identification of tumor. The outcomes show potential and promising results at lab scale evaluation through analyzing the vertebral datasets with Intra-Discal Pressure (IDP) images for experimental validation with 98.7% bending and 98.88 % segmentation accuracy leads to 94.2 ± 0.2 % to 97.02 ± 0.2 % average ROC grading.

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“…As technology has advanced and costs have dropped over the past decade, telemedicine is utilized to monitoring the patients remotely, without the need for long travels or inperson hospital visits. Also, it supports the transferring of vital biodata detected by biosensors or wearable sensors [1,2]. Modernized technology in healthcare utilizes the HIPAA video conferencing tools that helps to consult the patients [3,4].…”

Section: Introductionmentioning

confidence: 99%

Decentralized Telemedicine Framework for a Smart Healthcare Ecosystem

2020

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The healthcare sector is one of the most rapidly growing sectors globally. With the evergrowing technology, patient care, regulatory compliance, and digital transformation, there is an increased need for healthcare sectors to collaborate with all stakeholders-both within the healthcare ecosystem and in concurring industries. In recent times, telemedicine has proven to provide high quality, affordable, and predominantly adapted healthcare services. However, telemedicine suffers from several risks in implementation, such as data breach, restricted access across medical fraternity, incorrect diagnosis and prescription, fraud, and abuse. In this work, introduce blockchain-based framework that would unlock the future of the healthcare sector and improved services. Our proposed solution utilizing Ethereum smart contracts to develop a transparent, tamper-proof telemedicine healthcare framework, and ensure the integrity of sensitive patient data eliminating a central administrator. Moreover, the smart contract regulates the interaction between all the parties involved in the network and keeps the patient meticulously informed about the transactions in the network.

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Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction

Cited by 91 publications

References 26 publications

A Review of Piezoelectric and Magnetostrictive Biosensor Materials for Detection of COVID‐19 and Other Viruses

A Review of Piezoelectric and Magnetostrictive Biosensor Materials for Detection of COVID‐19 and Other Viruses

Vertebral Tumor Detection and Segmentation Using Analytical Transform Assisted Statistical Characteristic Decomposition Model

Decentralized Telemedicine Framework for a Smart Healthcare Ecosystem

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