Design of telemedicine information query system based on wireless sensor network

Gao, Qian; Gadekallu, Thippa Reddy

doi:10.4108/eetpht.v8i4.674

Cited by 13 publications

(11 citation statements)

References 22 publications

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“…With the advent of high-performance computing and storage solutions, ensemble models could be trained and deployed in the cloud to be used for real-time applications. The methods discussed in this study could be extended to a variety of natural and medical image recognition tasks [ 31 , 32 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs

2022

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Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical decision-making, and potentially result in improved patient treatment. The majority of works in the literature discuss training automatic segmentation models using coarse bounding box annotations. However, the granularity of the bounding box annotation could result in the inclusion of a considerable fraction of false positives and negatives at the pixel level that may adversely impact overall semantic segmentation performance. This study evaluates the benefits of using fine-grained annotations of TB-consistent lesions toward training the variants of U-Net models and constructing their ensembles for semantically segmenting TB-consistent lesions in both original and bone-suppressed frontal CXRs. The segmentation performance is evaluated using several ensemble methods such as bitwise- AND, bitwise-OR, bitwise-MAX, and stacking. Extensive empirical evaluations showcased that the stacking ensemble demonstrated superior segmentation performance (Dice score: 0.5743, 95% confidence interval: (0.4055, 0.7431)) compared to the individual constituent models and other ensemble methods. To the best of our knowledge, this is the first study to apply ensemble learning to improve fine-grained TB-consistent lesion segmentation performance.

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Section: Discussionmentioning

confidence: 99%

A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs

2022

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“…The convergence of telepresence, digital twinning and blockchain will reap amazing benefits of the Metaverse in healthcare, especially in terms of patient monitoring. Telepresence in medicine, also called as telemedicine, provides medical services remotely [186]. Test dummies of patients can be used in case of critical situations, so that even the responses to surgeries or medicines can be known way before it is provided to the patients in real.…”

Section: B Patient Monitoringmentioning

confidence: 99%

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

et al. 2023

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The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.INDEX TERMS Metaverse, healthcare, virtual reality, digital twin, cybersecurity.

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“…The concept [11], [37], [30] is as follows: Typically, if you have a problem, you must visit a doctor and wait in a controlled setting while they take some measurements. Your electrocardiogram, electromyogram, and blood pressure might all be monitored, which is obviously very inconvenient for many people because they have to take time off of work.…”

Section: Telemedicinementioning

confidence: 99%

Wireless Sensor Network Applications

Koffka¹

2022

IJFMR

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In this paper we explore some important applications of wireless sensor networks (WSNs). There are 8 Sections. In Section 2 we discuss structural monitoring. We present how WSNs are used in traffic control in Section 3. Section 4 shows the use of WSNs in the field of telemedicine. Pipeline monitoring with WSNs are illustrated in Section 5 and WSN precision agriculture in Section 6. In Section 7 WSNs usage in natural disaster monitoring is explored. Finally, the conclusion is given in Section 8.

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Design of telemedicine information query system based on wireless sensor network

Cited by 13 publications

References 22 publications

A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs

A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

Wireless Sensor Network Applications

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