Diagnostic Technologies Research Articles

Development of CNN-based sub-sonic speed vehicle body frame micro damage assessment technology using guided wave spectrogram images

Due to advancements in diagnostic technology, sub-sonic speed trains are subjected to significant fatigue stress on their bodies. This phenomenon can be considered a menace, leading to significant accidents on railways due to the tiny size of defects on the train bodies. In this research, a deep learning approach is employed to identify the initial damage status. The goal is to develop an evaluation technique for verifying an initial wall-thinning crack using frequency spectrogram images. The wall-thinning effects of 6.3 mm thick aluminum AL-2024 T6 plates, primarily used in frames and bodies, are confirmed through the frequency responses of guided waves. To verify the effects of wall thinning, both higher-order harmonic frequencies of guided waves and nonlinear ultrasonic techniques are simultaneously used. To identify the tendency of frequency response, frequency spectrogram images, illustrated by short-time Fourier transforming analysis, are chosen. For training the detection system using the Region-based Convolutional Neural Network (R-CNN), spectrogram images are generated for wall-thinning steps with an increment of 0.2 mm (3% of the total thickness). The ResNet-50 is employed in the convolutional layer of the R-CNN engine to address the issue of gradient vanishing. The results of this research indicate that the depth of wall thinning in the plates can be identified. Based on the R-CNN training set, the initial wall-thinning failure can be verified with reliable accuracy.

Brain tumour detection via EfficientDet and classification with DynaQ-GNN-LSTM

The early detection and accurate staging of brain tumors are critical for effective treatment strategies and improving patient outcomes. Existing methods for brain tumor classification often struggle with limitations such as suboptimal precision, accuracy, and recall rates, alongside significant delays in processing. The current methodologies in brain tumor classification frequently encounter issues such as inadequate feature extraction capabilities and limited accuracy in segmentation, which impede their effectiveness. To address these challenges, the proposed model integrates Fuzzy C-Means for segmentation, leveraging its ability to enhance the accuracy in distinguishing tumor regions. Bounding boxes surrounding identified tumour regions are produced by the method by efficiently utilising calculated region attributes. The use of Vision Transformers for feature extraction marks a significant advancement, offering a more nuanced analysis of the intricate patterns within brain imaging data samples. These features are then classified using a Dyna Q Graph LSTM (DynaQ-GNN-LSTM), a cutting-edge approach that combines the strengths of deep learning, reinforcement learning, and graph neural networks. The superiority of the proposed model is evident through its performance on multiple datasets. It demonstrates an 8.3% increase in precision, 8.5% increase in accuracy, 4.9% increase in recall and 4.5% increase in specificity, alongside 2.9% reduction in delay compared to existing methods. In conclusion, the proposed method offers an efficient solution to the challenges faced in brain tumor classification. The study's findings underscore the transformative impact of integrating cutting-edge technologies in medical diagnostics, paving the way for more accurate, and timely health interventions for clinical scenarios.

Electroencephalography as a new diagnostic technology for mild psychoneurological disorders

Owing to the increase in the information load in modem society, assessing the clinical characteristics of borderline psychopathological conditions is significantly difficult because we cannot objectively assess them using functional examination methods. To demonstrate the possibility of mathematical analysis of electroencephalography as a new method of clinical electroencephalographic diagnostics for clinically mild conditions, we examined the electroencephalogram indicators of patients who voluntarily and for the first time sought medical help with minimally pronounced clinical manifestations of psychoneurological diseases. The results obtained allowed us to determine that using modern methods of mathematical processing of electroencephalographic signals, we can identify objective differences in the activity indicators of individual cortical structures. The conducted studies allow us to provide practical medicine with a new objective approach to studying human cognitive function.

Deep Learning-Based Intelligent Diagnosis of Lumbar Diseases with Multi-Angle View of Intervertebral Disc

The diagnosis of degenerative lumbar spine disease mainly relies on clinical manifestations and imaging examinations. However, the clinical manifestations are sometimes not obvious, and the diagnosis of medical imaging is usually time-consuming and highly relies on the doctor’s personal experiences. Therefore, a smart diagnostic technology that can assist doctors in manual diagnosis has become particularly urgent. Taking advantage of the development of artificial intelligence, a series of solutions have been proposed for the diagnosis of spinal diseases by using deep learning methods. The proposed methods produce appealing results, but the majority of these approaches are based on sagittal and axial images separately, which limits the capability of different deep learning methods due to the insufficient use of data. In this article, we propose a two-stage classification process that fully utilizes image data. In particular, in the first stage, we used the Mask RCNN model to identify the lumbar spine in the spine image, locate the position of the vertebra and disc, and complete rough classification. In the fine classification stage, a multi-angle view of the intervertebral disc is generated by splicing the sagittal and axial slices of the intervertebral disc up and down based on the key position identified in the first stage, which provides more pieces of information to the deep learning methods for classification. The experimental results reveal substantial performance enhancements with the synthesized multi-angle view, achieving an F1 score of 96.67%. This represents a performance increase of approximately 15% over the sagittal images at 84.48% and nearly 14% over the axial images at 83.15%. This indicates that the proposed paradigm is feasible and more effective in identifying spinal-related degenerative diseases through medical images.

CRISPR-Based Assays for Point-of-Need Detection and Subtyping of Influenza

The high disease burden of influenza virus poses a significant threat to human health. Optimized diagnostic technologies that combine speed, sensitivity, and specificity with minimal equipment requirements are urgently needed to detect the many circulating species, subtypes, and variants of influenza at the point of need. Here, we introduce such a method using Streamlined Highlighting of Infections to Navigate Epidemics (SHINE), a clustered regularly interspaced shortpalindromic repeats (CRISPR)-based RNA detection platform. Four SHINE assays were designed and validated for the detection and differentiation of clinically relevant influenza species (A and B) and subtypes (H1N1 and H3N2). When tested on clinical samples, these optimized assays achieved 100% concordance with quantitative RT-PCR. Duplex Cas12a/Cas13a SHINE assays were also developed to detect two targets simultaneously. This study demonstrates the utility of this duplex assay in discriminating two alleles of an oseltamivir resistance (H275Y) mutation as well as in simultaneously detecting influenza A and human RNAse P in patient samples. These assays have the potential to expand influenza detection outside of clinical laboratories for enhanced influenza diagnosis and surveillance.

Multiplex PCR in septic arthritis and periprosthetic joint infections microorganism identification: Results from the application of a new molecular testing diagnostic algorithm.

Pathogen identification is key in the treatment of septic arthritis (SA) and periprosthetic joint infections (PJI). This study evaluates the outcome of the application of a new, score-based SA and PJI diagnostic algorithm, which includes the execution of molecular testing on synovial fluid. A score-based diagnostic algorithm, which includes serologic and synovial fluid markers determination using multiplex PCR (mPCR) and Next Generation Sequencing (NGS) molecular testing, has been applied to a consecutive series of patients with clinically suspected SA or PJI. Patients with a score ≥6 underwent synovial fluid molecular testing, together with traditional culture, to identify the pathogen and its genetically determined antibiotic resistance. One hundred and seventeen joints in 117 patients (62.5% women; average age 73 years) met the criteria for possible SA/PJI. The affected joint was the knee in 87.5% (joint replacement 66.5%; native joint 21%) and the hip in 12.5% (all replaced joints). 43/117 patients (36.7%) were ultimately diagnosed with SA/PJI. Among the various testing technologies applied, mPCR was the main determinant for pathogen identification in 63%, standard culture in 26%, and mNGS in 11%.Staphylococcus aureusand Enterococcus faecalis were the top two microorganisms identified by mPCR, whileStaphylococcus epidermidiswas the prevalent organism identified by NGS. mPCR detected the presence/absence of the genetically determined antibiotic resistance of all identified microorganisms. The average timeframe for pathogen identification was 3.13 h for mPCR, 4.5 days for culture, and 3.2 days for NGS. Molecular diagnostic technologies represent an innovative screening for fast microorganism identification when a joint infection is clinically suspected. Level IV, case series.

Integrating Radiological Diagnostic Tools in Shalya Tantra: Advancements and Clinical Applications

The integration of advanced diagnostic imaging technologies into Ayurvedic surgery marks a significant advancement, blending ancient principles from texts like Sushruta Samhita with modern innovations. This review examines the evolution and clinical applications of diagnostic imaging in Shalya Tantra, emphasizing the synergy between traditional diagnostic methods and contemporary modalities such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), artificial intelligence (AI), and augmented reality (AR). These technologies enhance diagnostic accuracy, surgical planning, and therapeutic outcomes across diverse medical specialties within Ayurveda. Case studies illustrate their pivotal role in diagnosing abdominal, pelvic, musculoskeletal, and obstetric conditions, guiding accurate interventions and improving patient care. Future directions explore the potential of AI-driven analytics, virtual simulations, and portable ultrasound devices to further optimize diagnostic capabilities and expand access to advanced imaging in Ayurvedic practice.

Follicular Fluid and Blood Monitorization of Infertility Biomarkers in Women with Endometriosis.

Infertility is recognized globally as a social disease and a growing medical condition, posing a significant challenge to modern reproductive health. Endometriosis, the third-most frequent gynecologic disorder, is one of the most common and intricate conditions that can lead to female infertility. Despite extensive research, the etiology, malignant transformation, and biological therapy of endometriosis remain unknown. Blood and follicular fluid are two matrices that have been carefully studied and can provide insights into women's health. These matrices are clinically significant because they contain metabolites closely associated with women's illness stage and reproductive outcomes. Nowadays, the application of metabolomic analysis in biological matrices may be able to predict the outcome of assisted reproductive technologies with greater precision. From a molecular viewpoint on reproductive health, we evaluate and compare the utilization of human follicular fluid and blood as matrices in analysis for diagnostic and assisted reproductive technology (ART) predictors of success for endometriosis patients. In the follicular fluid (FF), plasma, and serum of endometriosis-affected women, researchers identified dysregulations of oxidative stress, upregulation of several immune factors, and aberrations in energy metabolic pathways. The altered signatures negatively correlate with the overall oocyte and embryo quality and fertilization rate.

Appliance of digital technologies in diagnostics and prophylaxis of primary traumatic occlusion

Appliance of digital technologies in diagnostics and prophylaxis of primary traumatic occlusion

Advances in Femtosecond Coherent Anti-Stokes Raman Scattering for Thermometry

The combustion process is complex and harsh, and the supersonic combustion flow field is also characterized by short duration and supersonic speed, which makes the real-time diagnostic technology for the transient environment extremely demanding. It is of great significance to realize high time-resolved accurate measurement of temperature, component concentration, and other parametric information of the combustion field to study the transient chemical reaction dynamics of the combustion field. Femtosecond CARS spectroscopy can effectively avoid the collision effect between particles in the measurement process and reduce the influence of the non-resonant background to improve the measurement accuracy and realize the time-resolved measurement on a millisecond scale. This paper introduces the development history of femtosecond CARS spectroscopy, points out its advantages and disadvantages, and looks forward to the future development trend to carry out high time-resolved measurements, establish a database of temperature changes in various complex combustion fields, and provide support for the study of engine mechanisms.

Comprehensive Collection of Whole-Slide Images and Genomic Profiles for Patients with Bladder Cancer

Bladder cancer is one of the leading causes of cancer-related mortality in the urinary system. Understanding genomic information is important in the treatment and prognosis of bladder cancer, but the current method used to identify mutations is time-consuming and labor-intensive. There are now many novel and convenient ways to predict cancerous genomics from pathological slides. However, the publicly available datasets are limited, especially for Asian populations. In this study, we developed a dataset consisting of 75 Asian cases of bladder cancers and 112 Whole-Slide Images with one to two images obtained for each patient. This dataset provides information on the most frequently and clinically significant mutated genes derived by whole-exome sequencing in these patients. This dataset will facilitate exploration and development of novel diagnostic and therapeutic technologies for bladder cancer.

Self Operable E-health Diagnosis and Monitoring System with Gesture Control and Voice Assistance

In the present timeline, technology has advanced significantly, and the healthcare sector is constantly expanding. People in rural areas who cannot access corporate hospitals postpone their health check-ups due to the difficulty of traveling long distances. The readily available and expensive nature of diagnostic technologies have prevented many rural residents from seeking clinical examinations, even in cases when they have health issues. Corporate hospitals often make people to wait for long periods of time for health reports and causing delays. These issues highlighted the need for a device which aimed at developing an Arduino based health diagnosis system To resolve this issue, we have introduced a device that offers a comprehensive health diagnosis and monitoring system that uses Arduino technology to store and display vital health information. The system is designed to analyze electrocardiograms (ECG), photoplethysmograms (PPG), body temperature, heart rate and stress levels in real time and then process this information to create meaningful health status assessments and store them in the database for further health assessments. Furthermore, these data could be displayed through web server or an application. Additionally, this system provides voice assistance for the condition of patient when it is critical which helps in early detection of health conditions. Hand gestures are incorporated into this device, which makes it distinctive. Users can control the display of health parameters and easily move through the system's features with simple hand gestures. Users can switch between different types of modes in the system (such as on/off and different operation) by moving their hand in specified patterns. Thus, this makes the system user-friendly so that everyone may use it.

Visual detection of misfolded alpha-synuclein and prions via capillary-based quaking-induced conversion assay (Cap-QuIC)

Neurodegenerative protein misfolding diseases impact tens of millions of people worldwide, contributing to millions of deaths and economic hardships across multiple scales. The prevalence of neurodegenerative disease is predicted to greatly increase over the coming decades, yet effective diagnostics for such diseases are limited. Most diagnoses come from the observation of external symptoms in clinical settings, which typically manifest during relatively advanced stages of disease, thus limiting potential therapeutic applications. While progress is being made on biomarker testing, the underlying methods largely rely on fragile and expensive equipment that limits their point-of-care potential, especially in developing countries. Here we present Capillary-based Quaking Induced Conversion (Cap-QuIC) as a visual diagnostic assay based on simple capillary action for the detection of neurodegenerative disease without necessitating expensive and complex capital equipment. We demonstrate that Cap-QuIC has the potential to be a detection tool for a broad range of misfolded proteins by successfully distinguishing misfolded versus healthy proteins associated with Parkinson’s disease (α-synuclein) and Chronic Wasting Disease (prions). Additionally, we show that Cap-QuIC can accurately classify biological tissue samples from wild white-tailed deer infected with Chronic Wasting Disease. Our findings elucidate the underlying mechanism that enables the Cap-QuIC assay to distinguish misfolded protein, highlighting its potential as a diagnostic technology for neurodegenerative diseases.

Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study.

Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients. This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone. This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness. Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion. This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.

Development of a passive wireless sensor for fluidic detection and characterization utilizing the PCB-based coplanar electrode (PCE) configuration

During the global economic development, there's a growing focus on healthcare, especially in the advancement of medical diagnostic technologies, with a significant emphasis on glucose level evaluation. Glucose biosensors, predominantly electrochemical, have evolved over four generations, with the first three being enzyme-based and known for sensitivity and cost-effectiveness, albeit with limitations due to environmental susceptibility and reliance on enzyme activity. Recent advancements in non-invasive blood glucose monitoring, utilizing optical, microwave, and electrochemical techniques, offer diverse benefits without tissue penetration. Among these, impedance sensing stands out due to its flexibility and integration capability in handheld devices. This study proposes a wireless passive impedance method leveraging the inductor-capacitor (LC) sensing technique and PCB (Printed Circuit Board)-based coplanar electrode (PCE) configuration for fluidic sample detection. The proposed system integrates a two-coplanar-electrode layout with a square spiral inductor to assess fluidic conductivity and characterize various fluid types within samples. The effectiveness of this configuration was validated through experiments with NaCl and glucose solutions, confirming the feasibility of integrating PCB-based coplanar electrodes into conventional LC passive wireless sensing designs for fluidic detection and characterization.

Diagnosis and classification of kidney transplant rejection using machine learning-assisted surface-enhanced Raman spectroscopy using a single drop of serum

The quest to reduce kidney transplant rejection has emphasized the urgent requirement for the development of non-invasive, precise diagnostic technologies. These technologies aim to detect antibody-mediated rejection (ABMR) and T-cell-mediated rejection (TCMR), which are asymptomatic and pose a risk of potential kidney damage. The protocols for managing rejection caused by ABMR and TCMR differ, and diagnosis has traditionally relied on invasive biopsy procedures. Therefore, a convergence system using a nano-sensing chip, Raman spectroscopy, and AI technology was introduced to facilitate diagnosis using serum samples obtained from patients with no major abnormality, ABMR, and TCMR after kidney transplantation. Tissue biopsy and Banff score analysis were performed across the groups for validation, and 5 μL of serum obtained at the same time was added onto the Au–ZnO nanorod-based Surface-Enhanced Raman Scattering sensing chip to obtain Raman spectroscopy signals. The accuracy of machine learning algorithms for principal component-linear discriminant analysis and principal component-partial least squares discriminant analysis was 93.53% and 98.82%, respectively. The collagen (an indicative of kidney injury), creatinine, and amino acid-derived signals (markers of kidney function) contributed to this accuracy; however, the high accuracy was primarily due to the ability of the system to analyze a broad spectrum of various biomarkers.

The Value of Ultrasound in the Application of Water Enema in Intussusception

Objective: To introduce high-frequency ultrasound diagnostic technology to cooperate with diagnosis and treatment for patients with intussusception, and to observe its clinical application effect. Methods: The study took patients with intussusception as the object of observation, with a total of 52 cases participating, all of which were clinically admitted from December 2022 to December 2023. After enrollment, the patients underwent high-frequency ultrasound detection in sequence, recording the ultrasound imaging characteristics, and referring to the results of surgical or leaky saline enema reset to confirm the diagnosis (the gold standard) to assess the diagnostic efficacy of high-frequency ultrasound technology. Results: Comparing the confirmed diagnostic results of surgery or leaking saline enema restoration, the detection rate of high-frequency ultrasound technology was 98.07% (P &gt; 0.05). Among the 52 patients treated with hydropneumatic enema restoration with the cooperation of high-frequency ultrasound monitoring, 39 patients were successfully reset in the first treatment (75.00%), while 9 patients were successfully reset (17.30%) after multiple treatments (≥ 2 times); the remaining 4 patients were unsuccessful and then converted to surgical treatment, and all of them were successfully treated and cured. Conclusion: For the diagnosis of intussusception, high-frequency color Doppler ultrasound has the advantages of high accuracy and simple operation, which provides a scientific and accurate reference basis for the clinical diagnosis and treatment of patients.

Degradation study and diagnostic technology for Nafion membrane

Developing a fast diagnostic technology for membranes and accurately predicting the lifetime of membranes that can effectively reduce the manufacturing cost and overcome the technical barriers of proton exchange membrane fuel cell (PEMFC) lifetime. This paper focuses on developing an ex-situ accelerated chemical degradation method and investigating the impact of chemical degradation on membrane structure and fuel cell performance. The results show that voltage drop and hydrogen permeation current can detect the state online and predict the remaining lifetime of membrane electrode assembly (MEA). The aging rate is faster by three orders of magnitude than that of the published results by the established aging way. This study provides an additional decision for evaluating performance degradation and fuel cell system optimization, which could potentially lead to further improvements in the efficiency and durability of PEMFC systems.

Extent of investigation and management of cases of ‘unexplained’ mismatch repair deficiency (u-dMMR): a UK Cancer Genetics Group consensus

BackgroundMismatch repair deficiency (dMMR) is a characteristic feature of cancers linked to Lynch syndrome. However, in most cases, it results from sporadic somatic events rather than hereditary factors. The term...

Applications and advances in molecular diagnostics: revolutionizing non-tuberculous mycobacteria species and subspecies identification.

Non-tuberculous mycobacteria (NTM) infections pose a significant public health challenge worldwide, affecting individuals across a wide spectrum of immune statuses. Recent epidemiological studies indicate rising incidence rates in both immunocompromised and immunocompetent populations, underscoring the need for enhanced diagnostic and therapeutic approaches. NTM infections often present with symptoms similar to those of tuberculosis, yet with less specificity, increasing the risk of misdiagnosis and potentially adverse outcomes for patients. Consequently, rapid and accurate identification of the pathogen is crucial for precise diagnosis and treatment. Traditional detection methods, notably microbiological culture, are hampered by lengthy incubation periods and a limited capacity to differentiate closely related NTM subtypes, thereby delaying diagnosis and the initiation of targeted therapies. Emerging diagnostic technologies offer new possibilities for the swift detection and accurate identification of NTM infections, playing a critical role in early diagnosis and providing more accurate and comprehensive information. This review delineates the current molecular methodologies for NTM species and subspecies identification. We critically assess the limitations and challenges inherent in these technologies for diagnosing NTM and explore potential future directions for their advancement. It aims to provide valuable insights into advancing the application of molecular diagnostic techniques in NTM infection identification.