Towards a Machine-Learning-Assisted Dielectric Sensing Platform for Point-of-Care Wound Monitoring

Rahmani, Hamed; Archang, Maani M.; Jamali, Babak; Forghani, Mahdi; Ambrus, Aaron M.; Ramalingam, Deeban; Sun, Zhenjie; Scumpia, Philip O.; Coller, Hilary A.; Babakhani, Aydin

doi:10.1109/lsens.2020.2999031

Cited by 15 publications

(4 citation statements)

References 12 publications

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“…The algorithm had 98.17% accuracy. H. Rahmani et al [40] used a machine-learning algorithm to classify the permittivity of normal and wounded skin created by scratch, punch, and UVB burn using principal component analysis for data dimensionality reduction on the measured loss tangent (ε /ε ) data. Furthermore, they used the gaussian mixture model (GMM), an unsupervised learning method, support vector classifier (SVC), a supervised learning method, naïve Bayes, and neural net to classify between normal skin and punch wound and reported 97% to 100% accuracy of using these models.…”

Section: Kidneymentioning

confidence: 99%

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

et al. 2023

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Microwaves are non-ionizing electromagnetic radiation with waves of electrical and magnetic energy transmitted at different frequencies. They are widely used in various industries, including the food industry, telecommunications, weather forecasting, and in the field of medicine. Microwave applications in medicine are relatively a new field of growing interest, with a significant trend in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Artificial intelligence (AI)-enabled microwave systems can be developed to augment healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. An overview of recent developments in several areas of microwave applications in medicine, namely microwave imaging, dielectric spectroscopy for tissue classification, molecular diagnostics, telemetry, biohazard waste management, diagnostic pathology, biomedical sensor design, drug delivery, ablation treatment, and radiometry, are summarized. In this contribution, we outline the current literature regarding microwave applications and trends across the medical industry and how it sets a platform for creating AI-based microwave solutions for future advancements from both clinical and technical aspects to enhance patient care.

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Section: Kidneymentioning

confidence: 99%

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

et al. 2023

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“…In 2020, a machine-learning-assisted dielectric sensing platform for point-of-care wound monitoring system was proposed by Rahmani et al 31 They proposed solution to classify normal skin from wooded skin. They implemented machine-learning algorithms for classification task.…”

Section: State Of the Artmentioning

confidence: 99%

An IoT assisted clinical decision support system for wound healthcare monitoring

Sattar

Bajwa

Shafi

2021

Computational Intelligence

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“…An excellent testament to this statement is the emergence of IoT devices in various applications such as smart cities, intelligent agriculture, enhanced robotics, manufacturing, wearables, implants, and health monitoring systems. [1], [2], [3]. The use cases for some of these applications and their projected global economic value by 2035 are demonstrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Next-Generation IoT Devices: Sustainable Eco-Friendly Manufacturing, Energy Harvesting, and Wireless Connectivity

et al. 2023

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The work of Valentina Palazzi, Paolo Mezzanotte, and Luca Roselli was supported by the Italian Ministry of University and Research (MUR) through the PRIN Project "Development and promotion of the Levulinic acid and Carboxylate platforms by the formulation of novel and advanced PHA-based biomaterials and their exploitation for 3D printed green-electronics applications" under Grant 2017FWC3WC 003. This work did not involve human subjects or animals in its research.

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Towards a Machine-Learning-Assisted Dielectric Sensing Platform for Point-of-Care Wound Monitoring

Cited by 15 publications

References 12 publications

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

An IoT assisted clinical decision support system for wound healthcare monitoring

Next-Generation IoT Devices: Sustainable Eco-Friendly Manufacturing, Energy Harvesting, and Wireless Connectivity

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