Application of Data Mining and Machine Learning in Microwave Radiometry (MWR)

Levshinskii, Vladislav; Galazis, Christoforos; Ovchinnikov, Lev; Vesnin, Sergey; Losev, Alexander; Goryanin, Igor

doi:10.1007/978-3-030-46970-2_13

Cited by 21 publications

(29 citation statements)

References 37 publications

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“…We used as a starting model the same deep neural network (DNN) we have earlier applied for breast cancer diagnostics [ 30 , 31 , 32 ]. For this iteration, we further improved the dense model by incorporating characteristics from the Cascade Correlation Neural Network (CCNN).…”

Section: Resultsmentioning

confidence: 99%

Passive Microwave Radiometry for the Diagnosis of Coronavirus Disease 2019 Lung Complications in Kyrgyzstan

Osmonov

Ovchinnikov²,

Galazis

et al. 2021

Diagnostics

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The global spread of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19), could be due to limited access to diagnostic tests and equipment. Currently, most diagnoses use the reverse transcription polymerase chain reaction (RT-PCR) and chest computed tomography (CT). However, challenges exist with CT use due to infection control, lack of CT availability in low- and middle-income countries, and low RT-PCR sensitivity. Passive microwave radiometry (MWR), a cheap, non-radioactive, and portable technology, has been used for cancer and other diseases’ diagnoses. Here, we tested MWR use first time for the early diagnosis of pulmonary COVID-19 complications in a cross-sectional controlled trial in order to evaluate MWR use in hospitalized patients with COVID-19 pneumonia and healthy individuals. We measured the skin and internal temperature using 30 points identified on the body, for both lungs. Pneumonia and lung damage were diagnosed by both CT scan and doctors’ diagnoses (pneumonia+/pneumonia−). COVID-19 was determined by RT-PCR (covid+/covid−). The best MWR results were obtained for the pneumonia−/covid− and pneumonia+/covid+ groups. The study suggests that MWR could be used for diagnosing pneumonia in COVID-19 patients. Since MWR is inexpensive, its use will ease the financial burden for both patients and countries. Clinical Trial Number: NCT04568525.

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Section: Resultsmentioning

confidence: 99%

Passive Microwave Radiometry for the Diagnosis of Coronavirus Disease 2019 Lung Complications in Kyrgyzstan

Osmonov

Ovchinnikov²,

Galazis

et al. 2021

Diagnostics

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“…Details of the process are beyond this paper and will be described and published separately. See [28, 29] for more details.…”

Section: Resultsmentioning

confidence: 99%

“…It could be used for full body scan, including head (brain), wrist (cardiovascular), lung (respiratory), and guts (GI) to assess organ’s damage and eliminate risks in the COVID-19 rehabilitation stage. In the future, to improve the sensitivity and specificity it would be beneficial to use the same Deep Neural Network (DNN) we have earlier applied for breast cancer diagnostics [28,29], but more data are required.…”

Section: Discussionmentioning

confidence: 99%

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Passive Microwave Radiometry (MWR) for diagnostics of COVID-19 lung complications

Osmonov

Ovchinnikov²,

Galazis

et al. 2020

Preprint

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The World Health Organization (WHO) declared COVID-19 it as a global pandemic. It becomes clear that the virus is spreading mostly deadly due to limited access to diagnostics tests and equipment. Traditional radiography and CT remain the main methods of the initial examination of the chest organs. Now, most of the diagnostics has been focused on PCR, chest X-Ray/CT manifestations of COVID-19. However, there are problems with CT due to infection control issues, the inefficiencies introduced in CT room decontamination, and lack of CT availability in LMIC (Low Middle Income Countries). Passive microwave radiometry (MWR) is a cheap, non-radioactive and portable technology. It has already been used for diagnostics of cancer, and other diseases. We have tested if MWR could be used for early diagnostics of pulmonary COVID-19 complications. This was a randomized controlled trial (195 subjects) to evaluate the effectiveness of diagnostics using MWR in patients with pneumonia caused by COVID-19 while they are in hospitals of Kyrgyzstan, and healthy individuals. We have measured skin (IR) and internal (MWR) temperature by recording passive electromagnetic radiation through the chest wall in the projection of the lungs at 30 symmetrical points on both sides. Pneumonia and lung damage were diagnosed by X-RAY/CT scan and doctor final diagnosis (pn+/pn-). COVID-19 was determined by PCR test (covid+/covid-). The best results were obtained between pn-/covid- and pn+/covid+ groups with sensitivity 92% and specificity 75%. Overall, the study suggests that the use of MWR is a convenient and safe method for screening diagnostics in COVID-19 patients with suspected pneumonia. Since MWR is an inexpensive, it will ease the financial burden for both patients and the countries, especially in LMIC

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“…The problems solution provides the systems creation for the medical data interpretation and analysis [1]. Such systems, using machine learning methods and algorithms, should help specialists in making diagnoses and predicting the diseases development [2].…”

Section: Introductionmentioning

confidence: 99%

Application of Computer Simulation Results and Machine Learning in the Analysis of Microwave Radiothermometry Data

Polyakov¹,

Popov²,

Losev³

et al. 2021

MPCS

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This work was done with the aim of developing the fundamental breast cancer early differential diagnosis foundations based on modeling the spacetime temperature distribution using the microwave radiothermometry method and obtained data intelligent analysis. The article deals with the machine learning application in the microwave radiothermometry data analysis. The problems associated with the construction mammary glands temperature fields computer models for patients with various diagnostics classes, are also discussed. With the help of a computer experiment, based on the machine learning algorithms set (logistic regression, naive Bayesian classifier, support vector machine, decision tree, gradient boosting, Knearest neighbors, etc.) usage, the mammary glands temperature fields computer models set adequacy.

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Application of Data Mining and Machine Learning in Microwave Radiometry (MWR)

Cited by 21 publications

References 37 publications

Passive Microwave Radiometry for the Diagnosis of Coronavirus Disease 2019 Lung Complications in Kyrgyzstan

Passive Microwave Radiometry for the Diagnosis of Coronavirus Disease 2019 Lung Complications in Kyrgyzstan

Passive Microwave Radiometry (MWR) for diagnostics of COVID-19 lung complications

Application of Computer Simulation Results and Machine Learning in the Analysis of Microwave Radiothermometry Data

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