Internet of things enabled framework for terahertz and infrared cancer imaging

Gezimati, Mavis; Singh, Ghanshyam

doi:10.1007/s11082-022-04087-8

Cited by 16 publications

(9 citation statements)

References 19 publications

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“…The challenges associated with the implementation of Big Data technologies in healthcare have been extensively discussed in the literature. These include technical challenges related to data quality, processing capabilities, and integration, as well as organizational challenges such as change management, skill shortages, and cultural resistance [33]. Strategies to overcome these challenges have been proposed, including the development of comprehensive training programs for healthcare professionals and the adoption of change management practices to foster a culture of innovation [34].…”

Section: Related Workmentioning

confidence: 99%

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Zhussipbek,

Turymbetov,

Ibragimova

et al. 2024

IJACSA

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This research paper delves into the burgeoning field of Big Data analytics in healthcare, proposing an innovative framework aimed at refining the organization and management of medical processes within healthcare institutions. Through the lens of detailed case studies, including stroke diagnosis leveraging the UNet model, and the identification of heart and respiratory diseases via machine learning algorithms applied to data from wearable devices, the study illuminates the profound capabilities of Big Data technologies in enhancing the precision of diagnostics, tailoring patient treatment, and elevating the overall efficiency of healthcare services. It meticulously interprets the outcomes of these applications, discusses the practical implications for healthcare professionals and institutions, confronts the challenges inherent in the integration of sophisticated analytics in clinical settings, and outlines potential directions for future research. Among the pivotal challenges highlighted are issues related to data privacy, security, the need for advanced infrastructure, and the imperative for ongoing training and interdisciplinary cooperation to navigate the complexities of Big Data in healthcare. The paper underscores the transformative promise of Big Data analytics, suggesting that comprehensive adoption and adept implementation could revolutionize healthcare delivery, making it more personalized, efficient, and cost-effective. Through this exploration, the paper contributes to the ongoing discourse on the integration of technology in healthcare, offering insights into how Big Data analytics can serve as a cornerstone for the next generation of medical diagnostics and patient care management, thereby enhancing health outcomes on a global scale.

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Section: Related Workmentioning

confidence: 99%

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Zhussipbek,

Turymbetov,

Ibragimova

et al. 2024

IJACSA

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“…However, most of the machine learning models that have been explored in THz imaging and sensing for biomedical applications are based on shallow networks due to the unavailability of sufficient training datasets. The application of deep learning algorithms like the Convolutional Neural Networks (CNN) enables system complexity, robustness and multimodal capability [189]. The application of deep learning in this application has been explored in few studies such as [190] and [191].…”

Section: B Artificial Intelligence and Robotics In Thz Healthcarementioning

confidence: 99%

“…The application of deep learning in this application has been explored in few studies such as [190] and [191]. The development of custom convolutional neural network (CNN) model and fine-tuned, pretrained CNN models capable of multimodal image classification have been proposed in our recent studies [189], [192]. THz imaging systems integrated with Robotics have been recently explored for enhancing flexibility of THz systems for in vivo imaging of body geometry and locations that are not easily accessible.…”

Section: B Artificial Intelligence and Robotics In Thz Healthcarementioning

confidence: 99%

“…will revolutionize the healthcare for cancer care by moving from numerical data (vital physiological parameters) to the integration of spatial (image), temporal and physiological data domains towards personalized healthcare which is one of the goals of smart healthcare. In one of our studies [189], we proposed the framework of an IoT enabled CAD system for breast cancer. IoT simulation and cloud processing was performed in the ThingSpeak cloud platform with email alert capability.…”

Section: Internet Of Things and Wearable Technology In Thz Healthcarementioning

confidence: 99%

“…The application of machine learning algorithms, particularly deep learning techniques for THz data extraction, reconstruction and analysis tasks is a promising step towards improved sensing, speed, accurate localization performance and quality and intelligent THz system development. In the development of Computed Aided Diagnostic (CAD) systems, deep learning systems can help data and result visualization in a way that can be conveyed to medical practitioners to give them clinical decision support for making clear decisions [189]. However, the accuracy and effectiveness of deep learning-based implementations in THz cancer imaging for these data driven explorations require huge datasets for training.…”

Section: ) Challenges In Data Driven Thz Studiesmentioning

confidence: 99%

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Terahertz Imaging and Sensing for Healthcare: Current Status and Future Perspectives

Gezimati

Singh

2023

IEEE Access

Self Cite

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There is a keen interest in the exploration of new generation emitters and detectors due to advancements in innovation of new materials and device processing technologies which have opened up new frontiers in the Terahertz (THz) spectrum. Therefore, it is necessary to review the developments in THz technology for healthcare applications, their impact, implications and prospects for ongoing research and development. This paper provides a broad overview of the current status and prospects of application of THz imaging and sensing for the healthcare domain. We present current knowledge, identify existing challenges for wide scale clinical adoption of THz systems and prospective opinions to facilitate research and development towards optimized and miniaturized THz systems and biosensors that provide real operational convenience through emerging trends. Firstly, we provide an overview of the THz imaging and sensing techniques that exploit properties of THz generation and detection with emphasis on terahertz time domain spectroscopy (THz-TDS) and THz Metamaterials. The mechanisms of tissue image contrast and the application of THz imaging and sensing for biomedical applications in particular, the cancer detection application is reported. Secondly, an outlook toward the advancements in THz technology in the interface of healthcare 4.0 and its enabling technologies is explored for next generation smart and connected healthcare systems. Third, we identify the merits and existing challenges in THz cancer imaging and sensing and suggest prospective opinions to pave way to ongoing and future research. Further, we discuss the recent advances in THz imaging development and the contribution of near-field techniques based on plasmonic, and resonance based metasurfaces, waveguides etc. for breaking the diffraction limit towards development of THz systems that are convenient for point of care. We bring researchers a roadmap for future research scope.

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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Das,

Mazumdar,

Khondakar

et al. 2024

BMEMat

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Machine learning (ML) and nanotechnology interfacing are exploring opportunities for cancer treatment strategies. To improve cancer therapy, this article investigates the synergistic combination of Graphene Oxide (GO)‐based devices with ML techniques. The production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the investigation. GO is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal chemicals. Then, complicated biological data are analyzed using ML algorithms, which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular characteristics. The study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using ML. Predictive modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery systems. Furthermore, tracking treatment outcomes in real time is made possible by ML algorithms, which permit adaptive modifications to therapy regimens. By optimizing medication doses and delivery settings, the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

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Internet of things enabled framework for terahertz and infrared cancer imaging

Cited by 16 publications

References 19 publications

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Terahertz Imaging and Sensing for Healthcare: Current Status and Future Perspectives

Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

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