Non-invasive Monitoring Technique Research Articles

Informing conservation management of the bilby (Macrotis lagotis) in the Pilbara: a review of research and future directions

For threatened species that occur across multi-use landscapes, a coordinated cross-tenure management approach is desirable to achieve long-term conservation outcomes. To provide a comprehensive understanding of the progress towards achieving conservation priorities for the greater bilby (Macrotis lagotis) in the Pilbara region of Western Australia (WA), and inform ongoing research and funding investment, we review a research program that has focused on the species over the past decade. Priorities were initially established at a stakeholder workshop in 2013, and research targeting key areas has greatly enhanced ecological knowledge of the bilby in the Pilbara and elsewhere in WA. Highlights include the development of a non-invasive scat DNA-based abundance monitoring technique, an improved understanding of habitat use and diet, including the importance of Acacia spp. that host root-dwelling larvae, and the threat posed by introduced predators. Proposed future research directions focus on better understanding habitat requirements and the association between key food resources and fire, evaluating genetic structure and gene flow at the landscape-scale and examining threat interactions. Management to mitigate a range of threats including introduced predators, grazing livestock/feral herbivores and fire is recommended, with consideration for an integrated approach.

Correlation of salivary hs-CRP With conventional cardiovascular risk markers and hba1c in prediabetic subjects

Prediabetes has attracted considerable attention because it increases the risk of developing diabetes and cardiovascular disease. Prediabetes is also associated with obesity and dyslipidemia. Chronic low-grade inflammation is a crucial element in the clinical course of metabolic syndrome, and cardiovascular and cerebrovascular diseases. C-Reactive Protein (CRP) is a biomarker of inflammation that tends to increase in patients with diabetes and prediabetes. Most serological markers of diabetes are detected using invasive techniques that cause anxiety and pain. Therefore, the use of noninvasive techniques for frequent biomarker monitoring is necessary. Salivary diagnostics is an upcoming field for sensitive biomarker detection, such as hs-CRP. HbA1C is a valuable glycemic risk marker and has proven to be a reliable test of time. Both HbA1C and CRP play pivotal roles in diabetes and prediabetes. hs-CRP is a measure of CRP level with greater accuracy. The lower limit of its measurement is 0.01 mg/L and the measurement is more than100 times as sensitive than the CRP measurement (lower limit 5 mg/L). Therefore, our study aimed to evaluate and establish a relationship between these factors in prediabetics. Since metabolic syndrome and prediabetes are the cause and effect of each other, correlating anthropometric measurements and lipid profile with HbA1C and hs-CRP will be our focus.

Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey

Muscle fatigue represents a complex physiological and psychological phenomenon that impairs physical performance and increases the risks of injury. It is important to continuously monitor fatigue levels for early detection and management of fatigue. The detection and classification of muscle fatigue also provide important information in human-computer interactions (HMI), sports injuries and performance, ergonomics, and prosthetic control. With this purpose in mind, this review first provides an overview of the mechanisms of muscle fatigue and its biomarkers and further enumerates various non-invasive techniques commonly used for muscle fatigue monitoring and detection in the literature, including electromyogram (EMG), which records the muscle electrical activity during muscle contractions, mechanomyogram (MMG), which records vibration signals of muscle fibers, near-infrared spectroscopy (NIRS), which measures the amount of oxygen in the muscle, ultrasound (US), which records signals of muscle deformation during muscle contractions. This review also introduces the principle and mechanism, parameters used for fatigue detection, application in fatigue detection, and advantages and disadvantages of each technology in detail. To conclude, the limitations/challenges that need to be addressed for future research in this area are presented.

Ensemble Deep Learning Models for Heart Failure Prediction from Real Healthcare Records

Cardiovascular diseases (CVDs) remain the leading cause of death globally, necessitating advancements in non-invasive and efficient monitoring techniques. "Retinabeat" proposes a novel approach to CVD monitoring by leveraging the diagnostic potential of retinal vascular patterns. Utilizing state-of-the-art retinal imaging combined with the VGG19 convolutional neural network, this system aims to detect and analyze subtle changes in the retinal vasculature that correlate with cardiovascular health. Preliminary studies suggest that these retinal indicators can serve as reliable markers of cardiovascular anomalies, providing a non-invasive, cost-effective, and readily accessible method for early detection and ongoing management of heart health. This paper details the development and validation of the Retinabeat system, evaluates its performance against traditional methods, and discusses the implications of integrating advanced image processing technologies in routine cardiovascular care. The outcomes highlight the system’s potential to enhance predictive diagnostics and personalize health monitoring, paving the way for broader applications in telemedicine and preventive healthcare. Keywords— Cardiovascular diseases, VGG19, telemedicine, Retinal images

A Narrative Review on Financial Challenges and Health Care Costs Associated with Traumatic Brain Injury in the United States

Traumatic Brain Injury (TBI) is a highly prevalent and potentially severe medical condition. Challenges regarding TBI management are related to accurate diagnostics, defining its severity and establishing prompt interventions in order to impact outcomes. Among the healthcare components in TBI handling strategy is intracranial pressure (ICP) monitoring, which is fundamental to therapy decisions. However, ICP monitoring is an Aquiles tendon, imposing a significant financial burden on healthcare systems, particularly in middle and low-income communities. This review primarily investigates the economic burden of TBI and whether new technologies are suitable to reduce its healthcare costs without compromising the quality of care, according to the levels of evidence available MATERIAL AND METHODS: For this narrative review, the authors conducted a PUBMED search for articles discussing TBI healthcare costs, as well as monitoring technologies (tomography, magnetic resonance imaging, optic nerve sheath diameter, transcranial Doppler, pupillometry and noninvasive ICP waveform) and their application in managing TBI. Strategies were first evaluated from a medical noninferiority perspective before calculating the average savings of each selected strategy. All applicable papers were analyzed for quality using the Consolidated Health Economic Evaluation Reporting Standards 2022 Statement (CHEERS 2022)117 and the paper was written to conform as much as possible with it. The review included a total of 109 references and revealed a consistent potential in noninvasive technologies to reduce costs while maintaining or improving the quality of care. TBI prevalence has grown with an unproportionate healthcare burden in the last decades. Noninvasive monitoring techniques seem to be effective to reduce TBI healthcare costs, with no neglectable limitations, especially the need for more supporting scientific evidence. The clinical and financial undeniable potential of these techniques are compelling to further investigate their role in TBI management, as well as the creation of more comprehensive monitoring models to the understanding of complex phenomena occurring in the injured brain.

Using the gamma-index analysis for inter-fractional comparison of in-beam PET images for head-and-neck treatment monitoring in proton therapy: A Monte Carlo simulation study

Goal:In-beam Positron Emission Tomography (PET) is a technique for in-vivo non-invasive treatment monitoring for proton therapy. To detect anatomical changes in patients with PET, various analysis methods exist, but their clinical interpretation is problematic. The goal of this work is to investigate whether the gamma-index analysis, widely used for dose comparisons, is an appropriate tool for comparing in-beam PET distributions. Focusing on a head-and-neck patient, we investigate whether the gamma-index map and the passing rate are sensitive to progressive anatomical changes. Methods/materials:We simulated a treatment course of a proton therapy patient using FLUKA Monte Carlo simulations. Gradual emptying of the sinonasal cavity was modeled through a series of artificially modified CT scans. The in-beam PET activity distributions from three fields were evaluated, simulating a planar dual head geometry. We applied the 3D-gamma evaluation method to compare the PET images with a reference image without changes. Various tolerance criteria and parameters were tested, and results were compared to the CT-scans. Results:Based on 210 MC simulations we identified appropriate parameters for the gamma-index analysis. Tolerance values of 3 mm/3% and 2 mm/2% were suited for comparison of simulated in-beam PET distributions. The gamma passing rate decreased with increasing volume change for all fields. Conclusion:The gamma-index analysis was found to be a useful tool for comparing simulated in-beam PET images, sensitive to sinonasal cavity emptying. Monitoring the gamma passing rate behavior over the treatment course is useful to detect anatomical changes occurring during the treatment course.

Optical coherence tomography for multicellular tumor spheroid category recognition and drug screening classification via multi-spatial-superficial-parameter and machine learning.

Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies.

Noninvasive Monitoring of Tissue Temperature Changes Induced by Focused Ultrasound Exposure using Sparse Expression of Ultrasonic Radio Frequency Echo Signals

Abstract Background: Noninvasive therapies such as focused ultrasound were developed to be used for cancer therapies, vessel bleeding, and drug delivery. The main purpose of focused ultrasound therapy is to affect regions of interest (ROI) of tissues without any injuries to surrounding tissues. In this regard, an appropriate monitoring method is required to control the treatment. Methods: This study is aimed to develop a noninvasive monitoring technique of focused ultrasound (US) treatment using sparse representation of US radio frequency (RF) echo signals. To this end, reasonable results in temperature change estimation in the tissue under focused US radiation were obtained by utilizing algorithms related to sparse optimization as orthogonal matching pursuit (OMP) and accompanying Shannon’s entropy. Consequently, ex vivo tissue experimental tests yielded two datasets, including low-intensity focused US (LIFU) and high-intensity focused US (HIFU) data. The proposed processing method analyzed the ultrasonic RF echo signal and expressed it as a sparse signal and calculated the entropy of each frame. Results: The results indicated that the suggested approach could noninvasively estimate temperature changes between 37°C and 47°C during LIFU therapy. In addition, it represented temperature changes during HIFU ablation at various powers, ranging from 10 to 130 W. The normalized mean square error of the proposed method is 0.28, approximately 2.15 on previous related methods. Conclusion: These results demonstrated that this novel proposed approach, including the combination of sparsity and Shanoon’s entropy, is more feasible and effective in temperature change estimation than its predecessors.

Applications of Circulating Tumor DNA in Myelodysplastic Syndromes and Acute Myeloid Leukemia: Promises and Challenges.

The term 'liquid biopsy' has become widely used by clinicians with the development of non-invasive diagnostic and monitoring techniques for malignancies. Liquid biopsy can provide genetic information for early diagnosis, risk stratification, treatment selection and postoperative follow-up. In the era of personalized medicine, liquid biopsy is an important research direction. In recent years, research on circulating tumour DNA (ctDNA) in hematological malignancies has also made great progress. This review provides an overview of the current understanding of circulating tumour DNA in myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Additionally, recent advancements in the monitoring of minimal/measurable residual disease (MRD) through ctDNA are discussed.

Propofol effects over intracranial pressure waveform and cerebral hemodynamics: A case report

Introduction: Few reports have been dedicated on assessing cerebral hemodynamics (CH) and intracranial compliance (ICC) in non-primary neurological patients. Objective: The present study aims to observe the anesthetic influence of propofol on CH and ICC using noninvasive monitoring techniques in the immediate postoperative period. Methodology: This is a case report in which CH and ICC were assessed noninvasively using transcranial Doppler (TCD) and a skull deformation sensor (B4C) respectively, in the immediate postoperative period of a patient just after cardiac revascularization. After exclusion of volemic depletion, TCD and B4C parameters indicated reduction in cerebral blood flow what would be attributed to propofol infusion during surgery. After endovenous additional volume administration and arterial pressure elevation, TCD and B4C parameters improved. Final Considerations: It was possible observing cerebrovascular influences of propofol in the immediate postoperative period, by means of neurological ancillary techniques. Noninvasive neuromonitoring is a way to assess changes in brain physiology that may occur subtly and influence in non-primary neurological patients outcomes.

Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI.

Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications.

Utilization of Facial PPG Signals as a Novel Source for Blood Pressure Estimation

Hypertension, a prevalent global health concern, necessitates accurate and non-invasive blood pressure estimation techniques for effective monitoring and management. This paper proposes a novel machine learning approach utilizing Photo plethysmography (PPG) signals for precise blood pressure estimation. PPG signals, obtained conveniently through wearable devices, offer valuable physiological information related to cardiovascular activity. Leveraging advanced machine learning algorithms, including deep learning architectures and feature extraction methods, our proposed technique aims to establish a robust model for blood pressure estimation using facial image analysis. The methodology involves preprocessing PPG signals, extracting relevant features, and employing sophisticated machine learning models for regression analysis. The evaluation of this novel approach involves comprehensive experimentation with diverse datasets, ensuring its efficacy across various demographic groups and conditions. Results demonstrate promising accuracy and reliability in estimating blood pressure values, suggesting the potential for practical implementation in healthcare settings. The proposed technique showcases a promising avenue for non-invasive and accessible blood pressure monitoring, contributing significantly to personalized healthcare and continuous health monitoring systems.

Time-resolved near-infrared spectroscopy in monitoring acute ischemic stroke patients – Case study

Stroke is a leading cause of disability and death worldwide, with acute ischemic stroke (AIS) accounting for the majority of cases. Early and accurate diagnosis of AIS is crucial for improving patient outcomes. Non-invasive monitoring techniques, such as time domain near-infrared spectroscopy (tdNIRS), have shown potential for real-time monitoring of AIS patients at the bedside. However, there is a need for further research to evaluate the effectiveness of tdNIRS in the acute phase of stroke. In this study, we present the results of a case report using tdNIRS to monitor AIS patients without any additional stimulation. The tdNIRS technique allows for non-invasively assessing cerebral oxygenation in absolute units, enabling accurate measurement of changes in oxygenated and deoxygenated hemoglobin concentrations in the brain. Our aim was to determine the feasibility of tdNIRS in monitoring AIS patients.

Optical imaging techniques for non-invasive diagnosis and monitoring of disease

Optical imaging techniques for non-invasive diagnosis and monitoring of disease

Analysis of Non-Invasive Devices and Techniques Involved in Continuous Monitoring of Interstitial Glucose of Patients with Type 1 Diabetes

Abstract: There are 422 million diabetes patients worldwide according to 2023 census of WHO, and around 100 million cases in India. 8.4 million type 1 diabetes cases were recorded as of 2021. By 2040, it is projected that the number of people living with Type 1 Diabetes will reach 13.5–17.4 million. The surge in diabetes cases has created the critical need for regular glucose monitoring to effectively manage this prevalent health issue. Currently, glucose levels are predominantly measured invasively by pricking the finger for a blood sample and involves invasive methods, such as fingertip pricks, which can be uncomfortable and inconvenient, particularly for paediatric patients. This method utilizes blood glucose monitoring devices, which measure sugar levels in a small blood sample placed on a disposable test strip and have a certain degree of inaccuracy. Patients do not test as often as they should because of the pain and hassle of finger pricks and many do not achieve optimal glycaemic control. However, the shift toward non-invasive continuous glucose monitoring is becoming increasingly imperative and a promising alternative. With current biotechnological advances, many techniques offer a non-invasive, continuous means of monitoring glucose levels and have garnered significant attention in recent years. The objective is to consolidate and summarize recent advancements in non-invasive continuous glucose monitoring, emphasizing integration into wearable platforms for seamless and real-time monitoring. Furthermore, paediatric patients with Type 1 Diabetes face elevated risks of severe hypoglycaemic and hyperglycaemic events due to challenges in glycaemic control. Frequently, monitoring blood glucose is crucial to mitigate these risks and achieve optimal glycaemic management. The objective of ongoing research is to validate AI-based algorithms utilizing ECG signals collected through non-invasive devices to detect glycaemic events. These advancements, once commercially available, have the potential to revolutionize glucose monitoring, offering a convenient, efficient, and less intrusive approach, particularly benefiting paediatric patients and improving overall healthcare management. Despite substantial research and development, challenges persist, including standardizing sweat collection methods, addressing sample degradation, accounting for variations in sweat composition among individuals, optimizing diverse detection methods, improving glucose detection sensitivity, and ensuring overall commercial viability. Overcoming these challenges is paramount to bridge the gap between research and market availability, paving the way for a technological revolution in glucose monitoring. In this study, we compared potential non-invasive glucose monitoring devices and techniques to evaluate the most accurate, pain-free, fast, and cost-friendly device. Upon examination and survey, we have theorized the drawbacks persisting in the existing devices and hypothesized techniques. Looking forward this study has a broader application in not only understanding the limitations and creating more accurate devices but also in continuous glucose monitoring for diabetic patient care.

Characterization of the Cambaroides wladiwostokiensis Birstein & Vinogradov, 1934 (Decapoda: Astacidea) Mitochondrial Genome Using Genome Skimming and the Phylogenetic Implications within the Astacidea Infraorder.

The mitochondrial genome is a powerful tool for exploring and confirming species identity and understanding evolutionary trajectories. The genus Cambaroides, which consists of freshwater crayfish, is recognized for its evolutionary and morphological complexities. However, comprehensive genetic and mitogenomic data on species within this genus, such as C. wladiwostokiensis, remain scarce, thereby necessitating an in-depth mitogenomic exploration to decipher its evolutionary position and validate its species identity. The mitochondrial genome of C. wladiwostokiensis was obtained through shallow Illumina paired-end sequencing of total DNA, followed by hybrid assembly using both de novo and reference-based techniques. Comparative analysis was performed using available Cambaroides mitochondrial genomes obtained from National Center for Biotechnology Information (NCBI). Additionally, phylogenetic analyses of 23 representatives from three families within the Astacidea infraorder were employed using the PhyloSuite platform for sequence management and phylogenetic preparation, to elucidate phylogenetic relationships via Bayesian Inference (BI), based on concatenated mitochondrial fragments. The resulting genome, which spans 16,391 base pairs was investigated, revealing 13 protein-coding genes, rRNAs (12S and 16S), 19 tRNAs, and a putative control region. Comparative analysis together with five other Cambaroides mitogenomes retrieved from GenBank unveiled regions that remained unread due to challenges associated with the genome skimming technique. Protein-coding genes varied in size and typically exhibited common start (ATG) and stop (TAA) codons. However, exceptions were noted in ND5 (start codon: GTG) and ND1 (stop codon: TAG). Landscape analysis was used to explore sequence variation across the five available mitochondrial genomes of Cambaroides. Collectively, these findings reveal variable sites and contribute to a deeper understanding of the genetic diversity in this genus alongside the further development of species-specific primers for noninvasive monitoring techniques. The partitioned phylogenetic analysis of Astacidea revealed a paraphyletic origin of Asian cambarids, which confirms the data in recent studies based on both multilocus analyses and integrative approaches.

Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data

Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis.

Online Noninvasive Technique for Condition Monitoring of Cooling System in Urban Rail Vehicles

The open self-ventilated cooling system is an important component to dissipate heat in urban rail vehicles. While after long term operation, the cooling system will deteriorate, which ultimately affects the reliable operation of traction converter. In this paper, we propose a method to monitor the health status of cooling system in urban rail vehicles using the temperature response curve of negative temperature coefficient (NTC) thermistor built in IGBT module. Back Propagation Neural Network (BPNN) is employed to predict the reference curve of healthy state under different working conditions and Discrete Fréchet Distance (DFD) algorithm is applied to achieve the difference comparison. The proposed approach considers the dynamic response process and is suitable for online monitoring. Advantages of the proposed approach include no need to install additional measurement circuit and to heat the module to thermal equilibrium. Moreover, this method enables the concurrent monitoring of multiple components in the cooling system. Simulation analysis and experimental tests are performed to verify the accuracy and effectiveness.

A Non-Invasive Technique for Long-Term Monitoring of Gastroesophageal Reflux—A Pilot Study

Many people suffer from gastric or gastroesophageal reflux disorder (GERD) due to a malfunction of the cardia, the valve between the esophagus and the stomach. GERD is a syndrome caused by the ascent of gastric juices and bile from the stomach. This article proposes a non-invasive impedance measurement method and demonstrates the correlation between GERD and impedance variation between appropriately chosen points on the patient’s chest. This method is presented as an alternative to the most widely accepted diagnostic techniques for reflux, such as pH-metry, pH-impedance measurement, and esophageal manometry, which are invasive because they use a probe that is inserted through a nostril and reaches down to the esophagus.

A systematic review on the assessment of cerebral autoregulation in patients with Large Vessel Occlusion.

As the majority of large vessel occlusion (LVO) patients are not treated with revascularization therapies or efficiently revascularized, complementary management strategies are needed. In this article we explore the importance of cerebral autoregulation (CA) assessment in the prediction and/or modification of infarct growth and hemorrhagic transformation. In patients with LVO, these are important factors that affect prognosis. A systematic search of the PubMed, EMBASE databases and a targeted Google search was conducted, resulting in the inclusion of 34 relevant articles. There is an agreement that CA is impaired in patients with LVO; several factors have been identified such as time course, revascularization status, laterality, disease subtype and location, some of which may be potentially modifiable and affect outcomes. The personalized CA assessment of these patients suggests potential for better understanding of the inter-individual variability. Further research is needed for the development of more accurate, noninvasive techniques for continuous monitoring and personalized thresholds for CA.