Non-invasive Method Research Articles

A Comparative Study of the Rate of Helicobacter pylori infection Using Three Diagnostic Techniques among Patients in Rivers State

Background: Approximately 89% of gastric cancers are attributable to H. pylori infection, however, only 1 in 5 patients survive longer than 5 years after diagnosis. Evidence has shown that screening and eradication of H. pylori in young adults would be cost-effective and could prevent 1 gastric cancer in every 4 to 6 cases. Diagnostic tests used to detect Helicobacter pylori are either invasive or noninvasive methods. These tests have varying sensitivity and specificity, having about 90% accuracy in the diagnosis of H. pylori infection in clinical practice. Aim: This study aims at detecting Helicobacter pylori using antibody-antigen, stool antigen and polymerase chain reaction (PCR) and to compare the infection rate among the different diagnostic techniques. Methodology: This was a cross-sectional study. The participants included attendants to tertiary and private hospitals in Port Harcourt metropolis for medical treatment. Blood and stool samples were collected from one hundred and eighty (180) subjects aged 1 to 60 years and above. Ethical approval for the study was obtained from the Rivers State University Teaching Hospital Ethics Committee. Convenience sampling technique was deployed. Samples were collected from subjects who consented to the study. All samples collected were analyzed in Medical Microbiology Laboratory, Rivers State Teaching Hospital and subsequently, molecular analysis was carried out. Results: The blood antibodies test (serology) method shows a greater number of positive cases (43.9%) than stool antigen method (8.3%). Also, out of the 75% stool antigen positive samples, subjected to more specific PCR, only 25% were positive. There was a significant difference (p-value of <0.001) in sensitivity between antigen and serology, there was also a significant difference (p-value of <0.025) between antigen and PCR result outcomes. Conclusion: The study has shown that there is a notable variation in the diagnosis of H pylori among the laboratory techniques used.

Remote Monitoring in Heart Failure Patient: A Review of Non-invasive Approach

Remote monitoring (RM) has been actively developed in recent years, including in of patients with chronic heart failure. Unlike invasive methods, non-invasive methods do not involve surgical risks and offer a wide range of management options: telemonitoring, virtual visits, forward triage, hospital telemedicine, telerehabilitation, psychological support. The first RM was performed by a multidisciplinary team of healthcare providers from different specialties and showed effectiveness. Unfortunately, attempts to use modern technology to reduce human involvement have often been unsuccessful. However, the COVID-19 pandemic triggered a dramatic development of mHealth. Today, there is a huge variety of methods available, but it is still impossible to give a definitive answer about the effectiveness of non-invasive RM due to the lack of uniform standards, imperfect legislation, regional, social and economic differences in the availability of technology. Nevertheless, it was included in the European Society of Cardiology clinical guidelines for the diagnosis and treatment of acute and chronic heart failure in 2021 (IIb). This review focuses on the history of development, current and potentially effective methods, the problems they aim to solve, and the challenges society needs to overcome to use them effectively.

ECG signal reconstruction from PPG using a hybrid attention-based deep learning network

Electrocardiography (ECG) and photoplethysmography (PPG) are non-invasive methods used to quantify signals originating from the cardiovascular system. Although there is a strong correlation between the cycles of the two measures, the correlation between the waveforms has received little attention in research. Measuring the PPG is significantly simpler and more convenient compared to the ECG measure. Recent research has demonstrated that PPG signals can be utilized to rebuild the ECG signals, suggesting that healthcare professionals might acquire a comprehensive comprehension of patients' cardiovascular well-being by only measuring PPG. With the advancement of artificial intelligence, the deep learning model has made significant progress in reconstructing ECG signals from PPG signals. However, the quality of the reconstructed ECG signal is still in need of more enhancement. In order to enhance the quality of the reconstructed ECG signal, we propose an innovative hybrid attention-based bidirectional recurrent neural network (Bi-RNN) that incorporates a dilated convolutional neural network technique for the purpose of reconstructing ECG signals from PPG signals. This addresses issues that occur when standard dilated CNN (DCNN) models neglect the link between circumstances and dispersion of gradients. The suggested approach maximizes the utilization of the DCNN and Bi-RNN unit design to provide fusion features. In order to ensure the model's resilience to deformation, it is imperative to initially extract spatial properties utilizing convolutional neural networks (CNNs). Once we have obtained geographic data, we employ BiLSTM to extract temporal features. The proposed model is so-called hybrid attention-based CNN and BiLSTM (HA-CNN-BiLSTM). BiLSTM mitigates the issues of gradient vanishing and exploding while maintaining accuracy. To showcase the advantages of the suggested model, we conduct a comparison between HA-CNN-BiLSTM and each separate state-of-the-art method. The simulation results indicated that the proposed method yielded superior root mean square error (RMSE) when reconstructing the ECG signal across different optimizers. The results also demonstrated that the proposed method results in the lowest RMSE with SGDM optimizer which is 0.031.

Noninvasive, Multimodal Inflammatory Biomarker Discovery for Systemic Inflammation (NOVA Study): Protocol for a Cross-Sectional Study.

Prolonged systemic inflammation is recognized as a major contributor to the development of various chronic inflammatory diseases. Daily measurements of inflammatory biomarkers can significantly improve disease monitoring of systemic inflammation, thus contributing to reducing the burden on patients and the health care system. There exists, however, no scalable, cost-efficient, and noninvasive biomarker for remote assessment of systemic inflammation. To this end, we propose a novel, multimodal, and noninvasive approach for measuring inflammatory biomarkers. This study aimed to evaluate the relationship between the levels of inflammatory biomarkers in serum (gold standard) and those measured noninvasively in urine, sweat, saliva, exhaled breath, stool, and core body temperature in patients with systemic inflammation. This study is a single-center, cross-sectional study and includes a total of 20 participants (10 patients with systemic inflammation and 10 control patients). Eligible participants provide serum, urine, sweat, saliva, exhaled breath, and stool samples for biomarker analyses. Core body temperature is measured using a sensor. The primary end point is the level of C-reactive protein (CRP). The secondary end points are interleukin (IL)-1β, IL-6, IL-8, IL-10, and tumor necrosis factor-α levels. The tertiary end points are fractional exhaled nitric oxide, calprotectin, and core body temperature. Samples will be collected in 2 batches, enabling preliminary analysis of the first batch (patients 1-5 from each group). The full analysis will include both batches. CRP and cytokine levels will be measured using enzyme-linked immunosorbent assay and electrochemiluminescence immunoassay. For statistical analysis, the Shapiro-Wilk test will be used to evaluate the normality of the distribution in each variable. We will perform the 2-tailed t test or Wilcoxon rank sum test to compare the levels of inflammatory biomarkers between patients with systemic inflammations and control patients. Pearson and Spearman correlation coefficients will assess the relationship between inflammatory biomarkers from noninvasive methods and serum biomarkers. Using all-subset regression analysis, we will determine the combination of noninvasive methods yielding the highest predictive accuracy for serum CRP levels. Participants' preferences for sampling methods will be assessed through a questionnaire. The study received ethics approval from the independent research ethics committee of Canton Zurich on October 28, 2022. A total of 20 participants participated in the study measurements. Data collection started on February 22, 2023, and was completed on September 22, 2023. Participants were on average 52.8 (SD 14.4; range 24-82) years of age, and 70% (14/20) of them were women. The analysis results reporting findings are expected to be published in 2025. This study aims to evaluate the feasibility of noninvasive, multimodal assessment of inflammatory biomarkers in patients with systemic inflammation. Promising results could lead to the creation of noninvasive and potentially digital biomarkers for systemic inflammation, enabling continuous monitoring and early diagnosis of inflammatory activity in a remote setting. DERR1-10.2196/62877.

Non-invasive methods to assess seed quality based on ultra-weak photon emission and delayed luminescence.

Seed quality is the set of physical, genetic, and physiological characteristics, reflecting the overall germination potential. Maintaining an optimal seed quality is essential for agriculture and seed banks to preserve genetic diversity. Compared to conventional methods (e.g., germination tests), non-invasive approaches allow a more sustainable and rapid evaluation of seed quality but this is limited by high costs. The measurement of ultra-weak photon emission (UPE) and delayed fluorescence (DL), defined as biological phenomena potentially related to the physiological status of living systems, may represent a suitable approach to estimate seed quality. To test this hypothesis, seeds of five agriculturally relevant legume species (Phaseolus vulgaris L., Lathyrus sativus L., Cicer arietinum L., Pisum sativum L., and Vicia faba L.), stored at different conditions (room temperature or -18°C) for several years, were analysed using a LIANA© prototype to collect data regarding DL and UPE occurring after UV excitation. The obtained data were integrated with germination parameters which underline species-specific behaviours in response to storage conditions. The prediction models show variable efficiency in classifying seeds based on germination which underline species-dependent links between photon emission and seed quality. Therefore, these measurements represent novel, non-invasive, and rapid approaches to evaluate seed quality.

Diffusion weighted MRI and neutrophil lymphocyte ratio non-invasively predict infection in pancreatic necrosis: a pilot study.

Infected pancreatic necrosis (IPN) is a major determinant of mortality in acute pancreatitis (AP). Non-invasive diagnosis of IPN could guide the intervention in AP. We aimed to investigate the role of non-invasive methods like diffusion weighted magnetic resonance imaging (DW-MRI) and clinico-laboratory parameters as predictors of IPN. Prospective evaluation for predictors of IPN by diffusion restriction (DR) on DW-MRI and clinico-laboratory parameters was performed. Out of 39 patients included, 31 were analysed after exclusion. Twenty-six (83.8%) patients had moderately severe AP, and the rest had severe disease. They were categorized into Group A: patients with documented infection after intervention (n = 17) and Group B: successfully managed without intervention or negative culture after intervention (n = 14). On univariate analysis, Group A had significantly more incidence of fever (P = 0.020), persistent unwellness (P = 0.003), elevated neutrophil count (P = 0.007), lymphocyte count (P = 0.007), neutrophil lymphocyte ratio (NLR) (P = 0.028), DR on DW-MRI (P = 0.001) and low apparent diffusion coefficient (ADC) (P = 0.086). Multivariate analysis revealed DR on DW-MRI (P = 0.004) and NLR (P = 0.035) as significant predictors of IPN, among other factors. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DW-MRI were 94.1%, 78.6%, 91.66%, and 84.21%, respectively. The area under curve of NLR on the ROC plot was 0.85 and the best cutoff was >3.5, with sensitivity, specificity, PPV, and NPV of 70.6%, 78.6%, 80%, and 68.7% respectively. DW-MRI and NLR are promising non-invasive tools for accurate prediction of IPN and hence can guide the need for intervention in acute pancreatitis.

Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image

Xerostomia, commonly known as dry mouth, is characterized by reduced salivary secretion, which can lead to various oral health issues and discomfort. In this paper, we propose a novel, non-invasive method for predicting xerostomia through the analysis of tongue images. To predict salivary gland secretion from tongue images, we collected images from patients who visited the hospital with complaints of dry mouth and measured their saliva secretion. Features were extracted from these tongue images, and correlation analysis was performed using machine learning techniques to assess the relationship between the extracted features and measured saliva secretion. We obtained tongue images and saliva secretion measurements from 176 patients. Images were cropped to 100 × 100 pixels, resulting in 462 features. The dataset was divided into training and test sets, consisting of 160 and 16 samples, respectively. The correlation coefficients for the training and test datasets were 0.9496 and 0.9415, respectively, while the correlation coefficient for the entire dataset was 0.9482. The estimated linear equation was y = 0.9244x + 2.1664. This study aimed to predict salivary gland secretion based on tongue images. By extracting features from color images and employing a neural network machine learning model, we estimated salivary gland secretion. With a sufficiently large dataset of tongue images, further advancements in regression analysis using deep learning techniques could enhance the accuracy of these predictions.

Assessing the 9G Technology Blood Test for Predicting Lung Cancer in Patients with CT-Detected Lung Nodules: A Multicenter Clinical Trial

Background and Objectives: Lung nodules detected by chest computed tomography (CT) often require invasive biopsies for definitive diagnosis, leading to unnecessary procedures for benign lesions. A blood-based biomarker test that predicts lung cancer risk in CT-detected nodules could help stratify patients and direct invasive diagnostics toward high-risk individuals. Methods: In this multicenter, single-blinded clinical trial, we evaluated a test measuring plasma levels of p53, anti-p53 autoantibodies, CYFRA 21-1, and anti-CYFRA 21-1 autoantibodies in patients with CT-detected lung nodules. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated, and subgroup analyses by gender, age, and smoking status were performed. A total of 1132 patients who had CT-detected lung nodules, including 885 lung cancer cases and 247 benign lesions, were enrolled from two academic hospitals in South Korea. Results: The test demonstrated a sensitivity of 78.4% (95% CI: 75.7–81.1) and specificity of 93.1% (95% CI: 90.0–96.3) in predicting lung cancer in CT-detected nodules. The PPV was 97.6%, and the NPV was 54.6%. Performance was consistent across gender (sensitivity 79.3% in men and 76.8% in women) and age groups, with a specificity of 93.4% in men and 92.7% in women. Stage I lung cancer was detected with a sensitivity of 80.6%. Conclusions: The Lung Cancer test based on 9G technology presented here offers a non-invasive method for stratifying lung cancer risk in patients with CT-detected nodules. Its integration into clinical practice could reduce unnecessary interventions and foster earlier detection.

The assessment of urinary sexual hormones within minipuberty and correlations with anthropometrics in a cohort of healthy term children.

Minipuberty follows different trends in boys and girls. Aim of our study was to explore timing and dynamics of minipuberty in healthy infants, analyzing urinary levels of sexual hormones. Moreover, we analyzed the association among HPG axis activity and linear growth, ano-genital distances (AGDs) in both sexes, and penile length in males. Longitudinal cohort study in healthy term infants from birth to 6 months of life. Clinical evaluation (anthropometrics and AGDs) and urine sampling were performed at 0 (T0), 3 (T3), and 6 (T6) months. Urine samples were analyzed for gonadotropins and sex hormones. 165 participants were involved. The growth trend of our population was regular, as were the AGDs. Urinary hormones were correlated each other's. Specifically, in boys, the correlation coefficient between urinary FSH (uFSH) and urinary LH (uLH) decreased from T0 to T6, while between urinary Testosterone (uT) and uFSH increased. In girls, correlations between uFSH and urinary Estradiol (uE) were observed at each time point. Notably, several correlations between hormones and anthropometrics and AGDs were found; the most interesting correlation was found in males within uLH and PL (at T0 ρ: 0.323, p < 0.05 and at T3 ρ: 0.371, p < 0.01), whereas in females uFSH at T0 showed negative correlations with both length and body weight percentile at T3 (ρ: -0.505, p < 0.01 and ρ: -0.478, p < 0.01, respectively). Urinary matrix has proved to be a valuable, practical, non-invasive and cheaper method for sexual hormone assessment.

An Intraoperative Ultrasound Evaluation of Axillary Lymph Nodes: Cassandra Predictive Models in Patients with Breast Cancer—A Multicentric Study

Background and Objectives: Axillary lymph node (ALN) staging is crucial for the management of invasive breast cancer (BC). Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US is limited by intra- and inter-operator variability. This study aims to evaluate US and Elastosonography Shear Wave (SW-ES) parameters for ALN staging to develop a predictive model, named the Cassandra score (CS), to improve the interpretation of findings and standardize staging. Materials and Methods: Sixty-three women diagnosed with BC and treated at two Italian hospitals were enrolled in the study. A total of 529 lymph nodes were surgically removed, underwent intraoperative US examination, and were individually sent for a final histological analysis. The study aimed to establish a direct correlation between eight US-SWES features (margins, vascularity, roundness index (RI), loss of hilum fat, cortical thickness, shear-wave elastography hardness (SWEH), peripheral infiltration (PI), and hypoechoic appearance) and the histological outcome (benign vs. malignant). Results: Several statistical models were compared. PI was strongly correlated with malignant ALNs. An ROC analysis for Model A revealed an impressive AUC of 0.978 (S.E. = 0.007, p &lt; 0.001), while in Model B, the cut-offs of SWEH and RI were modified to minimize the risk of false negatives (AUC of 0.973, S.E. = 0.009, p &lt; 0.001). Model C used the same cut-offs as Model B, but excluded SWEH from the formula, to make the Cassandra model usable even if the US machine does not have SW-ES capability (AUC of 0.940, S.E. = 0.015, p &lt; 0.001). A two-tiered model was finally set up, leveraging the strong predictive capabilities of SWEH and RI. In the first tier, only SWES and RI were evaluated: a positive result was predicted if both hardness and roundness were present (SWES &gt; 137 kPa and RI &lt; 1.55), and conversely, a negative result was predicted if both were absent (SWES &lt; 137 kPa and RI &gt; 1.55). In the second tier, if there was a mix of the results (SWES &gt; 137 kPa and RI &gt; 1.55 or SWES &lt; 137 kPa and RI &lt; 1.55), the algorithm in Model B was applied. The model demonstrated an overall prediction accuracy of 90.2% in the training set, 87.5% in the validation set, and 88.9% across the entire dataset. The NPV was notably high at 99.2% in the validation set. This model was named the Cassandra score (CS) and is proposed for the clinical management of BC patients. Conclusion: CS is a simple, non-invasive, fast, and reliable method that showed a PPV of 99.1% in the malignancy prediction of ALNs, potentially being also well suited for young sonographers.

AI-Driven Cardiomegaly Detection Using Cutting-Edge Chest X–ray imagery

This project aims to develop an innovative approach for detecting cardiomegaly, a condition characterized by an enlarged heart, using machine learning techniques, specifically Support Vector Machines (SVM), and state-of-the-art chest X-ray imagery. Cardiomegaly serves as a significant indicator of various cardiac diseases, and early detection through accurate imaging analysis can lead to timely intervention and improved patient outcomes. The proposed machine learning solution utilizes SVM algorithms trained on a diverse dataset of chest X-ray images to automatically identify signs of cardiomegaly with high precision and reliability. By harnessing the power of SVM, this system offers a non-invasive and cost-effective method for screening large populations for cardiac abnormalities, facilitating early diagnosis and appropriate medical management. Key features of the developed solution include robust feature extraction techniques, real-time analysis, and integration with existing healthcare infrastructure for seamless implementation in clinical settings. Moreover, the system prioritizes patient privacy and data security by adhering to relevant regulations and standards governing medical imaging and information management. Overall, this project represents a significant advancement in the field of cardiac health monitoring, providing healthcare professionals with a valuable tool for early detection and management of cardiomegaly, ultimately improving patient care and reducing the burden of cardio vascular disease

In-hospital bioimpedance-derived total body water predicts short-term cardiovascular mortality and re-hospitalizations in acute decompensated heart failure patients.

Hospital re-admissions in heart failure (HF) patients are mostly caused by an acute exacerbation of their chronic congestion. Bioimpedance analysis (BIA) has emerged as a promising non-invasive method to assess the volume status in HF. However, its correlation with clinically assessed volume status and its prognostic value in the acute intra-hospital setting remains uncertain. In this single-center observational study, patients (n = 49) admitted to the cardiology ward for acute decompensated HF (ADHF) underwent a daily BIA-derived volume status assessment. Median hospital stay was 7 (4-10) days. Twenty patients (40%) reached the composite endpoint of cardiovascular mortality or re-hospitalization for HF over 6months. Patients at discharge displayed improved NYHA class, lower body weight, plasma and blood volume, as well as lower NT-proBNP levels compared to the admission. Compared to patients with total body water (TBW) less than or equal to that predicted by body weight, those with higher relative TBW levels had elevated NT-proBNP and E/e´ (both p < 0.05) at discharge. In the Cox multivariate regression analysis, the BIA-derived delta TBW between admission and discharge showed a 23% risk reduction for each unit increase (HR = 0.776; CI 0.67-0.89; p = 0.0006). In line with this finding, TBW at admission had the highest prediction importance of the combined endpoint for a subgroup of high-risk HF patients (n = 35) in a neural network analysis. In ADHF patients, BIA-derived TBW is associated with the increased risk of HF hospitalization or cardiovascular death over 6months. The role of BIA for prognostic stratification merits further investigation.

A Deep Learning Method to Integrate extracelluar miRNA with mRNA for cancer studies.

Extracellular miRNAs (exmiRs) and intracellular mRNAs both can serve as promising biomarkers and therapeutic targets for various diseases. However, exmiR expression data is often noisy, and obtaining intracellular mRNA expression data usually involves intrusive procedures. To gain valuable insights into disease mechanisms, it is thus essential to improve the quality of exmiR expression data and develop non-invasive methods for assessing intracellular mRNA expression. We developed CrossPred, a deep-learning multi-encoder model for the cross-prediction of exmiRs and mRNAs. Utilizing contrastive learning, we created a shared embedding space to integrate exmiRs and mRNAs. This shared embedding was then used to predict intracellular mRNA expression from noisy exmiR data and to predict exmiR expression from intracellular mRNA data. We evaluated CrossPred on three types of cancers and assessed its effectiveness in predicting the expression levels of exmiRs and mRNAs. CrossPred outperformed the baseline encoder-decoder model, exmiR or mRNA-based models, and variational autoencoder models. Moreover, the integration of exmiR and mRNA data uncovered important exmiRs and mRNAs associated with cancer. Our study offers new insights into the bidirectional relationship between mRNAs and exmiRs. The datasets and tool are available at https://doi.org/10.5281/zenodo.13891508. Supplementary data are available at Bioinformatics online.

Monitoring C–C coupling in catalytic reactions via machine-learned infrared spectroscopy

Abstract Tracking atomic structural evolution along chemical transformation pathways is essential for optimizing chemical transitions and enhancing control. However, molecule-level knowledge of structural rearrangements during chemical processes remains a great challenge. Here, we couple infrared spectroscopy as a non-invasive method to probe molecular transformations, with a machine-learned protocol to immediately map the spectroscopic fingerprints to atomistic structures. From the theoretical perspective, we demonstrate it here with the example of C–C coupling in catalytic reactions, elucidating various structural conformations along dynamic trajectories. Within the transferable application to the specific CO–CO dimerization reaction, the structural and energetic variations of the critical chemical species could be identified via infrared spectroscopy. This approach extends the power of spectroscopy from fingerprinting chemical configurations to using them for assigning dynamic structural information.

Drainage Fluid Amylase as a Biomarker for the Detection of Anastomotic Leakage After Low Anterior Resection of Rectal Cancer: A Two-center Study.

This study aimed to investigate the utility of measuring amylase levels in drainage fluid (DFA) for early, non-invasive detection of anastomotic leakage (AL) in undergoing low anterior resection (LAR) for rectal cancer. This prospective observational cohort study analyzed drainage fluid samples from patients who underwent LAR for rectal cancer at two medical centers between February 2021 and December 2023. DFA levels were measured on postoperative days (PODs) 1, 3, and 5. AL was confirmed by clinical evidence and radiological imaging. Statistical analyses were performed to evaluate the diagnostic performance of DFA. Of 120 LAR cases, AL occurred in five (4.16%). DFA levels on POD 3 and 5 were significantly higher in the AL than in the non-AL group (p<0.0001). DFA on POD 5 had the highest diagnostic accuracy for early AL detection, with an area under the curve of 0.99, achieving 100% sensitivity and 99.5% specificity at a cutoff of 846 U/l. A DFA >846 U/l predicted AL with negative predictive and positive predictive values of 83.3% and 100%, respectively, on POD 5. Measuring DFA is a non-invasive, simple and cost-effective method for early AL detection in patients with rectal cancer undergoing LAR. Our findings also suggested that drain placement may be useful for the early detection of AL through DFA measurement.

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.

A Review on Kidney Stone Detection using ML and DL Techniques

Kidney stone detection is a critical healthcare challenge, as timely and accurate diagnosis can prevent complications. The motivation behind this review is the increasing prevalence of kidney stones and the need for more effective, non-invasive detection methods. Machine learning (ML) and deep learning (DL) techniques offer promising solutions, leveraging medical imaging data to enhance diagnostic accuracy. However, limitations such as high computational cost and reliance on large datasets hinder their full potential. The aim of this review is to analyze the latest advancements in kidney stone detection using ML and DL techniques. The objective is to compare existing methodologies, highlight their strengths and weaknesses, and suggest future research directions, particularly in integrating transfer learning and fine-tuning techniques to enhance performance.

Comparison of the Effects of Sanyinjiao Point (SP6) Acupressure with Combined SP6 and LI4 Acupressure on Labor Pain Intensity in Primiparous Women: A Randomized Controlled Trial

Background: Childbirth is an important event and special experience in women's lives. Labor pain is the most severe pain that women undergo in their lives. Pain management during labor is crucial for the well-being of the mother and fetus. Objective: This study aimed to compare the effect of combined LI4 and SP6 acupressure with SP6 acupressure on reducing labor pain. Methods: A total of 108 primiparous women who participated in the study were randomized into three groups, combining LI4 and SP6 acupressure, SP6 acupressure, and a control group. Pain intensity was evaluated before, immediately, 1 and 2 hours after the intervention and the second stage of labor by visual analogue scale (VAS). Results: Although the pain intensity immediately after the intervention was lower in the combined SP6 and LI4 acupressure group than in the other groups, the differences were not significant between the three groups (p = 0.147). The pain intensity 1 hour after the intervention and in the second stage of labor had a statistically significant difference between the three groups (p =0.006, p =0.001). The pain intensity 2 hours after the intervention was lower in the combined SP6 and LI4 acupressure group than the other groups, but it was not statistically significantly different (p= 0.05). The pain intensity 1 hour after the intervention was not statistically different between the SP6 group and the control group (p= 0.095). The pain intensity 2 hours after the intervention was lower in the combined SP6 and LI4 acupressure group compared to the control group, but it was not significant (p=0.06). The pain intensity in the second stage of labor was lower in the combined SP6 and LI4 acupressure group and SP6 acupressure group compared to the control group (p =0.001, p = 0.017), but there was no significant difference between the SP6 acupressure group and combined SP6 and LI4 acupressure group (p=1.000). Conclusion: The obtained results revealed that combination intervention is a safe, effective, noninvasive and economical method in alleviating labor pain intensity. Healthcare providers are expected to master the acupressure technique as an alternative, complementary therapy, which can be used as an alternative or additional therapy.

Rotary excitation of non-sinusoidal pulsed magnetic fields: Towards non-invasive direct detection of cardiac conduction.

There is a need for high resolution non-invasive imaging methods of physiologic magnetic fields. The purpose of this work is to develop a MRI detection approach for non-sinusoidal magnetic fields based on the rotary excitation (REX) mechanism which was previously successfully applied for the detection of oscillating magnetic fields in the sub-nT range. The new detection concept was examined by means of Bloch simulations, evaluating the interaction effect of spin-locked magnetization and low-frequency pulsed magnetic fields. The REX detection approach was validated under controlled conditions in phantom experiments at 3 T. Gaussian and sinc-shaped stimuli were investigated. In addition, the detection of artificial fields resembling a cardiac QRS complex, which is the most prominent peak visible on a magnetocardiogram, was tested. Bloch simulations demonstrated that the REX method has a high sensitivity to pulsed fields in the resonance case, which is met when the spin-lock frequency coincides with a non-zero Fourier component of the stimulus field. In the experiments, we found that magnetic stimuli of different durations and waveforms can be distinguished by their characteristic REX response spectrum. The detected REX amplitude was proportional to the stimulus peak amplitude (R2 > 0.98) and the lowest field detection was 1 nT. Furthermore, the detection of QRS-like fields with varying QRS durations yielded significant results in a phantom setup (p < 0.001). REX detection can be transferred to non-sinusoidal pulsed magnetic fields and could provide a non-invasive, quantitative tool for spatially resolved assessment of cardiac biomagnetism. Potential applications include the direct detection and characterization of cardiac conduction.

The Utilization of Diagnostic Musculoskeletal Ultrasound in the Evaluation for Ischiofemoral Impingement: A Perspective for Rehabilitation Providers

Ischiofemoral impingement (IFI) is a significant but often underdiagnosed source of deep posterior hip pain and functional impairment, frequently associated with narrowing of the ischiofemoral space (IFS) between the ischial tuberosity and the lesser trochanter of the femur. Traditionally, diagnostic approaches for IFI have relied on clinical examination and magnetic resonance imaging (MRI). However, the advent of diagnostic musculoskeletal ultrasound (MSKUS) has transformed the evaluation process. MSKUS offers a non-invasive, cost-effective, and dynamic method for assessing the IFS and associated musculature in real time, allowing for high-resolution visualization of soft tissues, bones, and neurovascular structures. For rehabilitation providers, understanding the application, strengths, and limitations of MSKUS can enhance diagnostic accuracy, guide targeted therapeutic interventions, and potentially expedite recovery. This article reviews the anatomy of the IFS, the pathophysiology of IFI, and the use of MSKUS in diagnosing IFI, equipping rehabilitation professionals with the knowledge to integrate this valuable diagnostic tool into clinical practice.