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.

Background and Objective: Medical microwave radiometry (MWR) is used to capture the thermal properties of internal tissues and has usages in breast cancer detection. Our goal in this paper is to improve classification performance and investigate automated neural architecture search methods. Methods: We investigated extending the weight agnostic neural network by optimizing the weights using the bi-population covariance matrix adaptation evolution strategy (BIPOP-CMA-ES) once the topology was found. We evaluated and compared the model based on the F1 score, accuracy, precision, recall, and the number of connections. Results: The experiments were conducted on a dataset of 4912 patients, classified as low or high risk for breast cancer. The weight agnostic BIPOP-CMA-ES model achieved the best average performance. It obtained an F1-score of 0.933, accuracy of 0.932, precision of 0.929, recall of 0.942, and 163 connections. Conclusions: The results of the model are an indication of the promising potential of MWR utilizing a neural network-based diagnostic tool for cancer detection. By separating the tasks of topology search and weight training, we can improve the overall performance.

Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD₅ is the amount of oxygen consumed in 5 days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this research is to apply microbial fuel cells (MFCs) to reduce the time requirement of BOD₅ measurements. An artificial neural network (ANN) has been created, and the predictions we obtained for BOD₅ measurements were carried out within 6–24 h with an average error of 7%. The outcomes demonstrated the viability of our AI MFC/BES BOD₅ sensor in real-life scenarios.

The functional analysis of the left atrium (LA) is important for evaluating cardiac health and understanding diseases like atrial fibrillation. Cine MRI is ideally placed for the detailed 3D characterization of LA motion and deformation but is lacking appropriate acquisition and analysis tools. Here, we propose tools for the Analysis of Left Atrial Displacements and DeformatIons using online learning neural Networks (Aladdin) and present a technical feasibility study on how Aladdin can characterize 3D LA function globally and regionally. Aladdin includes an online segmentation and image registration network, and a strain calculation pipeline tailored to the LA. We create maps of LA Displacement Vector Field (DVF) magnitude and LA principal strain values from images of 10 healthy volunteers and 8 patients with cardiovascular disease (CVD), of which 2 had large left ventricular ejection fraction (LVEF) impairment. We additionally create an atlas of these biomarkers using the data from the healthy volunteers. Results showed that Aladdin can accurately track the LA wall across the cardiac cycle and characterize its motion and deformation. Global LA function markers assessed with Aladdin agree well with estimates from 2D Cine MRI. A more marked active contraction phase was observed in the healthy cohort, while the CVD LVEF↓ group showed overall reduced LA function. Aladdin is uniquely able to identify LA regions with abnormal deformation metrics that may indicate focal pathology. We expect Aladdin to have important clinical applications as it can non-invasively characterize atrial pathophysiology. All source code and data are available at: https://github.com/cgalaz0 1/aladdin_cmr_la.

Background and Objective: Medical Microwave Radiometry (MWR) is used to capture the thermal properties of internal tissues and has usages in breast cancer detection. Our goal in this paper is to improve classification performance and investigate automated neural architecture search methods. Methods: We investigate optimizing the weights of a weight agnostic neural network using bi-population covariance matrix adaptation evolution strategy (BIPOP-CMA-ES) once the topology is found. We compare it against a weight agnostic and cascade correlation neural network. Results: The experiments are conducted on a breast cancer dataset of 4912 patients. Our proposed weight agnostic BIPOP-CMA-ES model achieved the best performance. It obtained an F1-score of 0.9225, accuracy of 0.9219, precision of 0.9228, recall of 0.9217 and topology of 153 connections. Conclusions: The results are an indication of the potential of MWR utilizing a neural network-based diagnostic tool for cancer detection. By separating the tasks of topology search and weight training, we are able to improve the overall performance.

The World Health Organization (WHO) declared COVID-19 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) temperatures 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 Summary statement: Categorization of COVID-19 caused pneumonia suspicion by MWR has good diagnostic perspectives. It could be done in clinics or for mass screening to identify potential COVID-19 patients with lung complications. Background: The use of chest CT for COVID-19 and PCR diagnosis in healthcare settings with limited PCR and CT capacity is controversial. MWR categorization of the level of COVID-19 suspicion of lung complications might improve diagnostic performance. Purpose: To investigate the value of MWR in addition to CT and COVID-19 PCR scans and to determine its diagnostic performance in individuals with COVID-19 symptoms during hospital admission and rehabilitation. Materials and Methods: In this trial (Kyrgyz Committee Clinical Trial Number: 01-2/141 27 May 2020), from June, 1 2020 to August, 1 2020, we performed parallel MWR, PCR and CT tests, for individuals with COVID-19 admitted to the hospital for medical emergencies related to COVID-19 and pneumonia suspicion. Siemens Ecoline CT scanner, and HITACHI, Radnext 50 Chest X-Ray was used. RT-PCR test were done using “DNA technology” company https://www.dna-technology.ru/equipmentpr/nabory-reagentov-dlya-pcr-infekcii-respiratornogo-trakta/sars-cov-2sars-cov ). For MWR and IR measurements RTM-01-RES was used MMWR LTD, UK ( www.mmwr.co.uk ) Results: This was a randomized controlled trial to evaluate the effectiveness of diagnostics of COVID-19 (covid-/covid+) and pneumonia (pn+/pn-) using passive microwave radiometry (MWR) in patients while they are in hospital, and healthy individuals. We have measured internal (MWR) and skin (IR) temperature on 195 subjects. 149 of them were hospitalized with pneumonia symptoms to Medical center of KSMA and BICARD clinic. Pneumonia and lung damage were diagnosed by X-RAY/CT scans and radiologists lung damage assessment (pn+/pn-). COVID-19 was determined by PCR test (covid+/covid-). The best diagnostics results were obtained between pn-/covid- and pn+/covid+ groups with sensitivity 92% and specificity 75%. Conclusion: 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 inexpensive, it will ease the financial burden for both patients and the countries, especially in LMIC.