Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives
Abstract
:1. Introduction
2. Search Strategy
3. Diagnostic Applications of Microwaves
3.1. Microwave Imaging (MWI) Techniques
- Qualitative: It uses confocal microwave imaging and radar imaging algorithms where every single antenna is used to transmit and receive its own scattered signal. This technique has shown promising results so far. In oncology, its utility to detect malignant breast tissue was elicited by Oloumi et al. using the time-domain UWB circular-SAR technique [10]. Another study conducted by Grzegorczyk TM et al. in 2012 revealed the first 3D reconstructed image of breast tissue using microwaves within a timeframe of twenty minutes [11]. Surprisingly, attempts have also been made to use it in acute care settings to identify the site of brain stroke. The associated edema or hemorrhage causes up to 20% alteration in dielectric properties of the brain tissue [12]. Another common pathology we encounter very frequently in clinical practice is osteoporosis, which is very prevalent in post-menopausal females and elderly populations. The existing gold standard investigation used for its diagnosis, i.e., dual X-ray absorptiometry (DXA), exposes the patient to radiation and fails to assess bone quality, which is dictated by microarchitecture, composition, and the degree of microdamage. These problems can be easily circumvented by MWI, as shown in a study by Amin et al. on weight-bearing trabecular calcaneal bone [13]. However, even with the immense potential to evolve into a prominent diagnostic tool, using microwave energy for imaging needs extensive research as not many studies are available to properly evaluate dielectric properties of all the body tissues, facilitate clinical translation of these measurements, and address its potential limitations.
- Quantitative: also known as microwave tomography (MT): It relies on the tissue dielectric properties (relative permittivity and conductivity) to create an image of the tissue using a set of antennas where one of them is used to illuminate the tissue and others gather the scattered waves. On a technical aspect, MWI is plagued by the inverse electromagnetic (EM) scattering problem during the processing of the data for image reconstruction. Typically, iterative inversion methods such as the Born iterative method (BIM), distorted Born iterative method (DBIM), contrast source inversion, etc., are used, but even with advances in numerical methods, solving the inverse problem is still challenging due to slow convergence, non-linearities, and ill-posedness leading to false solutions and unstable outcomes. This difficulty is further complicated by the 3D nature of the imaging domain, increasing the computational demand and processing times [14,15]. This is where deep learning (DL), a subset of artificial intelligence (AI), comes to the rescue, as it can quickly reconstruct the images within a few seconds or minutes, making the overall process suitable for real-time applications. According to L Ahmadi et al., DL approaches have been proven to be twice as fast for similar accuracy thresholds compared to conventional iterative methods [16]. AI is a rapidly evolving field with new architectures and approaches demonstrated by researchers in different areas. Many studies reveal a variety of DL architectures for MWI. Xudong Chen et al. authored a comprehensive review of different types of DL approaches explicitly used for solving inverse EM problems [17]. However, solving this inverse scattering problem in a 3D domain, at high resolution and dynamic range, is still a big challenge where AI can play a crucial role.
3.2. Microwaves in Diagnostic Pathology
3.3. Microwave-Based Molecular Diagnostics
3.4. Dielectric Spectroscopy Applications
3.4.1. Breast
3.4.2. Liver
3.4.3. Kidney
3.4.4. Lungs
3.4.5. Machine Learning to Solve Analytical Problems
3.5. Microwave Radiometry in Medicine
4. Applications of Microwaves in Treatment
4.1. Microwave Ablation
4.1.1. Liver
4.1.2. Bone
- Tumors
- Osteomyelitis
4.1.3. Uterus
- Menorrhagia
- Fibroids
4.1.4. Prostate
4.1.5. Kidney
4.1.6. Adrenal
4.1.7. Thyroid
4.1.8. Lung
4.1.9. Heart
4.2. Microwave Ablation with AI
5. Microwave Energy in Drug Delivery
6. Microwaves in Telemetry
7. Microwaves in Hospital Waste Management
8. Microwave Hardware Design
- i.
- hardware—the antenna system that collects microwave signals reflected from tissues
- ii.
- software techniques that recreate an image of the object [117].
9. Discussion
9.1. Microwave Imaging Hardware Design with AI
9.2. AI-Assisted Dielectric Spectroscopy
9.3. AI-Assisted Molecular Diagnostic Using Microwaves
9.4. AI-Assisted Telemetry Using Microwaves
9.5. AI-Assisted Hospital Waste Management Using MW
9.6. Microwaves in the Field of Pathology Leveraging AI
9.7. Microwave-Based Medical Sensors with AI
9.8. AI-Assisted Drug Delivery Using Microwaves
9.9. AI-Assisted Microwave Ablation
9.10. AI-Assisted Microwave Radiometry
10. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Application | Sensor Design | Frequency | Reference | |
---|---|---|---|---|
Blood Pressure Estimation | Continuous-wave radar sensor | The analysis is conducted by reflective pulse transit time (R-PTT) using the BP computation algorithm. | 24 GHz | [127] |
Wrist Pulse Sensor | The sensor creates a focused electric field to detect wrist pulse waveforms in the near-field region. Then, the reflective pulse transit time is taken from this measured wrist pulse waveform and uses the blood pressure computational algorithm. | 5.7 GHz | [128] | |
Dielectric Characterization | Microwave biochemical sensor | Circular substrate integrated waveguide (CSIW) topology. | 1 to 6 GHz | [129] |
Microwave near-field sensor | The sensor is based on a small planar resonator and developed in a complete-cycle topology optimization where a binary particle swarm algorithm is applied. | 5.63 GHz | [130] | |
High-resolution probe | The probe is designed based on a small loop antenna which is loaded by spiral resonator. | 915 MHz | [131] | |
Liquid Dielectric Characterization | Split-Ring Resonator Sensor | A small volume of liquid is considered to conduct complex permittivity (ε′ + jε ″) characterization. | Up to frequencies of approximately 200 MHz | [132] |
MW sensor with Metamaterial Complementary Split Ring Resonator | A contactless sensor is proposed by using liquid samples placed normally on the sensor surface. The sample is placed inside capillary glass tubes to determine the dielectric properties of liquids. The samples that were placed inside the tubes changed the resonant frequency of the CSRR sensor. | 2.4 GHz | [133] | |
Blood Glucose Level Monitoring | Millimeter-Wave Radar Sensor | The radar’s several channels are used to gather the reflected mm waves, which serve as distinctive signatures for the internal synthesis and composition of the examined blood samples. Signal-processing techniques are used to distinguish between various glucose concentrations and link them to the reflected mm-wave data. | 60 GHz | [134] |
Ultra-wide band transceiver | Non-invasive estimation is achieved by using UWB planar antenna as hardware and ANN with the signal acquisition as a software module. | 4.7 GHz | [135] | |
Non-invasive microwave sensor | An in house open-ended coaxial cable is used, and the complex permittivity values are determined with the help of ANN from the value of complex reflection coefficients. Debye complex permittivity model is used. | 0.3 to 15 GHz | [136] | |
Spiral microstrip resonator | An analytical new equation is constructed with the help of Newton–Raphson iterative method. | 300 MHz to 2 GHz | [137] | |
Avian Influenza Virus | Biosensing metamaterial reflector | Different complex refractive indexes (CRIs) are detected | 1.71464 THz | [138] |
Kidney stones (renal calculi) | Open-ended contact probe | Newton–Raphson method is used to fit Cole–Cole parameters to the dielectric properties and k-nearest-neighbors (kNN) machine-learning algorithm is used for the classification. | 500 MHz to 6 GHz | [39] |
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Gopalakrishnan, K.; Adhikari, A.; Pallipamu, N.; Singh, M.; Nusrat, T.; Gaddam, S.; Samaddar, P.; Rajagopal, A.; Cherukuri, A.S.S.; Yadav, A.; et al. Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives. Electronics 2023, 12, 1101. https://doi.org/10.3390/electronics12051101
Gopalakrishnan K, Adhikari A, Pallipamu N, Singh M, Nusrat T, Gaddam S, Samaddar P, Rajagopal A, Cherukuri ASS, Yadav A, et al. Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives. Electronics. 2023; 12(5):1101. https://doi.org/10.3390/electronics12051101
Chicago/Turabian StyleGopalakrishnan, Keerthy, Aakriti Adhikari, Namratha Pallipamu, Mansunderbir Singh, Tasin Nusrat, Sunil Gaddam, Poulami Samaddar, Anjali Rajagopal, Akhila Sai Sree Cherukuri, Anmol Yadav, and et al. 2023. "Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives" Electronics 12, no. 5: 1101. https://doi.org/10.3390/electronics12051101
APA StyleGopalakrishnan, K., Adhikari, A., Pallipamu, N., Singh, M., Nusrat, T., Gaddam, S., Samaddar, P., Rajagopal, A., Cherukuri, A. S. S., Yadav, A., Manga, S. S., Damani, D. N., Shivaram, S., Dey, S., Roy, S., Mitra, D., & Arunachalam, S. P. (2023). Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives. Electronics, 12(5), 1101. https://doi.org/10.3390/electronics12051101