Traditional Diagnostic Methods Research Articles

Adaptive spectrum amplitude modulation method for rolling bearing fault frequency determination

Abstract Signal preprocessing and feature extraction are decisive factors in determining the frequency of bearing faults. The presence of noise interference in the status signal of rolling bearings often hampers accurate fault detection. Although there are various methods for preprocessing vibration signals in rolling bearings, they need further improvement in terms of enhancing fault feature expression and localizing fault frequency bands. This limitation significantly hinders the accuracy of fault frequency determination. In order to enhance the representation of fault information on the frequency spectrum, this study proposes a combined approach that incorporates sparse stacked autoencoder (SSAE), wavelet packet decomposition (WPD), and adaptive spectrum amplitude modulation (ASAM). The resulting method is referred to as SSAE-WPD-ASAM. Firstly, the bearing vibration signal is decomposed by wavelet packet according to the scale and frequency band of the signal. On this basis, the signal reconstruction is realized based on the wavelet packet coefficient and energy distribution in different frequency bands. Secondly, for the whole life cycle signal, the reconstructed signal is self-encoded by sparse stacked autoencoder to achieve dimensionality reduction of the reconstructed signal. Then, the spare reconstructed signal is subjected to ASAM. Finally, through envelope demodulation, peak detection of fault frequency and empirical fault frequency comparison, the specific fault types of rolling bearings are determined. The proposed method is verified by theoretical simulation and three groups of practical experiments. The results show that the proposed method has a significant improvement in diagnostic efficiency and accuracy compared with traditional diagnostic methods.

Molecular Detection of Respiratory Tract Viruses in Chickens at the Point of Need by Loop-Mediated Isothermal Amplification (LAMP)

Accurate and timely molecular diagnosis of respiratory diseases in chickens is essential for implementing effective control measures, preventing the spread of diseases within poultry flocks, minimizing economic loss, and guarding food security. Traditional molecular diagnostic methods like polymerase chain reaction (PCR) require expensive equipment and trained personnel, limiting their use to centralized labs with a significant delay between sample collection and results. Loop-mediated isothermal amplification (LAMP) of nucleic acids offers an attractive alternative for detecting respiratory viruses in broiler chickens with sensitivity comparable to that of PCR. LAMP’s main advantages over PCR are its constant incubation temperature (∼65 °C), high amplification efficiency, and contaminant tolerance, which reduce equipment complexity, cost, and power consumption and enable instrument-free tests. This review highlights effective LAMP methods and variants that have been developed for detecting respiratory viruses in chickens at the point of need.

Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification

Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis. Leveraging the HAM10000 dataset, a comprehensive collection of dermatoscopic images encompassing a diverse range of skin lesions, this study introduces a sophisticated CNN model tailored for the nuanced task of skin lesion classification. The model’s architecture is intricately designed with multiple convolutional, pooling, and dense layers, aimed at capturing the complex visual features of skin lesions. To address the challenge of class imbalance within the dataset, an innovative data augmentation strategy is employed, ensuring a balanced representation of each lesion category during training. Furthermore, this study introduces a CNN model with optimized layer configuration and data augmentation, significantly boosting diagnostic precision in skin cancer detection. The model’s learning process is optimized using the Adam optimizer, with parameters fine-tuned over 50 epochs and a batch size of 128 to enhance the model’s ability to discern subtle patterns in the image data. A Model Checkpoint callback ensures the preservation of the best model iteration for future use. The proposed model demonstrates an accuracy of 97.78% with a notable precision of 97.9%, recall of 97.9%, and an F2 score of 97.8%, underscoring its potential as a robust tool in the early detection and classification of skin cancer, thereby supporting clinical decision-making and contributing to improved patient outcomes in dermatology.

Clinical Characteristics, Prognosis Factors and Metagenomic Next-Generation Sequencing Diagnosis of Mucormycosis in patients With Hematologic Diseases.

New diagnostic methods and antifungal strategies may improve prognosis of mucormycosis. We describe the diagnostic value of metagenomic next⁃generation sequencing (mNGS) and identify the prognostic factors of mucormycosis. We conducted a retrospective study of hematologic patients suffered from mucormycosis and treated with monotherapy [amphotericin B (AmB) or posaconazole] or combination therapy (AmB and posaconazole). The primary outcome was 84-day all-cause mortality after diagnosis. Ninety-five patients were included, with "proven" (n = 27), "probable" (n = 16) mucormycosis confirmed by traditional diagnostic methods, and "possible" (n = 52) mucormycosis with positive mNGS results. The mortality rate at 84 days was 44.2%. Possible + mNGS patients and probable patients had similar diagnosis processes, overall survival rates (44.2% vs 50.0%, p = 0.685) and overall response rates to effective drugs (44.0% vs 37.5%, p = 0.647). Furthermore, the median diagnostic time was shorter in possible + mNGS patients than proven and probable patients (14 vs 26 days, p < 0.001). Combination therapy was associated with better survival compared to monotherapy at six weeks after treatment (78.8% vs 53.1%, p = 0.0075). Multivariate analysis showed that combination therapy was the protective factor (HR = 0.338, 95% CI: 0.162-0.703, p = 0.004), though diabetes (HR = 3.864, 95% CI: 1.897-7.874, p < 0.001) and hypoxemia (HR = 3.536, 95% CI: 1.874-6.673, p < 0.001) were risk factors for mortality. Mucormycosis is a life-threatening infection. Early management of diabetes and hypoxemia may improve the prognosis. Exploring effective diagnostic and treatment methods is important, and combination antifungal therapy seems to hold potential benefits.

A comprehensive review of early detection of COVID-19 based on machine learning and deep learning models

This paper reviews the use of machine learning (ML) and deep learning (DL) for early coronavirus disease (COVID-19) detection, highlighting their potential to overcome the limitations of traditional diagnostic methods such as long processing times and high costs. We analyze studies applying ML and DL to imaging, clinical, and genomic data, assessing their performance in terms of accuracy, sensitivity, specificity, and efficiency. The review discusses the advantages, limitations, and challenges of these models, including data quality, generalizability, and ethical considerations. It also suggests future research directions for improving model efficacy, such as integrating multi-modal data and developing more interpretable models. This concise review serves as a guide for researchers, healthcare practitioners, and policymakers on the advancements and prospects of ML and DL in early COVID-19 detection, promoting further innovation and collaboration in this vital public health domain.

Noninvasive assessment of liver fibrosis in mini pigs using an 18F–AlF-NOTA-RGD2 PET/CT molecular probe

To evaluate the efficacy of the 18F–AlF-NOTA-RGD2 positron emission tomography (PET)/computed tomography (CT) molecular probe for the noninvasive staging of liver fibrosis in mini pigs, a potential alternative to invasive diagnostic methods was revealed. This study used 18F–AlF-NOTA-RGD2 PET/CT imaging of mini pigs to assess liver fibrosis. The methods included synthesis and quality control of the molecular probe, establishment of an animal model of liver fibrosis, blood serum enzymatic tests, histopathological examination, PET/CT imaging, collagen content and expression, and mitochondrial reserve function assessment. The 18F–AlF-NOTA-RGD2 PET/CT molecular probe effectively differentiated various stages of liver fibrosis in mini pigs. Blood serum enzymatic tests revealed distinct stages of liver fibrosis, revealing significant increases in AST, ALT, TBIL, and DBIL levels as fibrosis advanced. Notably, ALT levels increased markedly in severe fibrosis patients. A gradual increase in collagen deposition and increasing α-SMA RNA expression and protein levels effectively differentiated between mild and severe fibrosis stages. Pathological examinations and Sirius Red staining confirmed these findings, highlighting substantial increases in collagen accumulation. PET/CT imaging results aligned with histopathological findings, showing that increased radiotracer uptake correlated with fibrosis severity. Assessments of mitochondrial function revealed a decrease in total liver glutathione content and mitochondrial reserve capacity, especially in patients with severe fibrosis. The 18F–AlF-NOTA-RGD2 PET/CT molecular probe is a promising tool for the noninvasive assessment of liver fibrosis, offering potential benefits over traditional diagnostic methods in hepatology.

A fault diagnosis method with AT-ICNN based on a hybrid attention mechanism and improved convolutional layers

Fault diagnosis is crucial for mechanical systems, with early diagnosis of bearings playing a key role in ensuring the overall safety and smooth operation of the mechanical system. However, in real industrial environments, traditional diagnostic methods limit the extraction of fault signals from rotating machinery. This study aims to improve the fault diagnosis method for critical mechanical components and proposes a novel deep learning model, the Attention Improved CNN (AT-ICNN) fault diagnosis method. The method combines Convolutional Neural Network (CNN) and attention mechanism to extract key fault feature information from signals, enhancing the model’s ability to highlight fault features and capture global information. This improves the accuracy of fault type identification. The AT-ICNN model enhances traditional CNN models by introducing Improved Convolutional (IMConv) and integrating a hybrid attention mechanism to effectively extract relevant fault information. Experimental results demonstrate superior diagnostic performance of AT-ICNN on the CWRU bearing dataset and laboratory bearing dataset, with accuracy rates of 98.12% and 98.72%, respectively. This represents about 9% improvement over baseline models and other advanced methods. In-depth analysis of experimental results validates the significant advantages of AT-ICNN in the field of fault diagnosis for critical mechanical components.

A Hybrid Study for Epileptic Seizure Detection Based on Deep Learning using EEG Data

Epilepsy, a neurological disease characterized by recurrent seizures, can be diagnosed using Electroencephalogram (EEG) signals. Traditional diagnostic methods often face limitations, leading to delays and potential misdiagnoses. In response, researchers have been developing low-cost assistive systems to enhance diagnostic accuracy and reduce life-threatening risks for epilepsy patients. In this study, a hybrid approach is proposed to diagnose epilepsy disease. To validate the success of the proposed algorithm, Hauz Khas and Bonn data sets were used. AlexNet, GoogleNet, VGG19, ResNet50, and ResNet101 classifiers were employed in this study along with the Continuous Wavelet Transform (CWT) and Short Time Fourier Transform (STFT). To increase the generalization capability, 10-fold cross-validation method was used in the classification process. Firstly, the preictal and ictal moments in the Hauz Khas dataset was classified with 99.5% success rate by CWT method and Resnet101. Similarly, 99.8% accuracy was achieved in the binary classification of the Bonn dataset using the CWT method with Resnet101. Finally, for the classification with the AB-CD-E group, 99.33% classification success rate was achieved by using the CWT method with the Resnet-101 model. These findings underscore the potential of the proposed assistive system to significantly improve the diagnosis and management of epilepsy, demonstrating high accuracy and reliability across different datasets and classification techniques.&amp;nbsp;

Heart Disease Prediction Using Machine Learning

Heart disease remains a leading cause of mortality worldwide, necessitating improved methods for early detection and risk assessment. This paper reviews and analyzes the application of machine learning techniques in heart disease prediction, focusing on five primary algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. By examining existing studies and datasets, we evaluate the effectiveness of these algorithms in predicting heart disease risk. Our analysis demonstrates that machine learning models can significantly enhance the accuracy and efficiency of heart disease prediction compared to traditional diagnostic methods. The Random Forest algorithm exhibited the highest overall performance, with studies reporting accuracy rates up to 95% in identifying potential heart disease cases. This review highlights the potential of machine learning in revolutionizing cardiovascular healthcare by enabling more personalized risk assessments and facilitating early intervention strategies. The integration of these advanced predictive models into clinical practice could substantially improve patient outcomes and reduce the global burden of heart disease. Keywords: Cardiovascular Risk Prediction, Machine Learning Algorithms, Electronic Health Records, Random Forest, Artificial Neural Networks, Feature Importance, Clinical Decision Support, Personalized Medicine, Predictive Analytics in Healthcare, Early Disease Detection.

Pre- and post-surgery: advancements in artificial intelligence and machine learning models for enhancing patient management in infective endocarditis.

Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of symptoms and has a complicated pattern of occurrence, leading to a significant number of deaths. IE poses significant diagnostic and treatment difficulties. This evaluation examines the utilization of artificial intelligence (AI) and machine learning (ML) models in addressing information extraction (IE) management. It focuses on the most recent advancements and possible applications. Through this paper, we observe that AI/ML can significantly enhance and outperform traditional diagnostic methods leading to more accurate risk stratification, personalized therapies as well and real-time monitoring facilities. For example, early postsurgical mortality prediction models like SYSUPMIE achieved 'very good' area under the curve (AUROC) values exceeding 0.81. Additionally, AI/ML has improved diagnostic accuracy for prosthetic valve endocarditis, with PET-ML models increasing sensitivity from 59% to 72% when integrated into ESC criteria and reaching a high specificity of 83%. Furthermore, inflammatory biomarkers such as IL-15 and CCL4 have been identified as predictive markers, showing 91% accuracy in forecasting mortality, and identifying high-risk patients with specific CRP, IL-15, and CCL4 levels. Even simpler ML models, like Naïve Bayes, demonstrated an excellent accuracy of 92.30% in death rate prediction following valvular surgery for IE patients. Furthermore, this review provides a vital assessment of the advantages and disadvantages of such AI/ML models, such as better-quality decision support approaches like adaptive response systems on one hand, and data privacy threats or ethical concerns on the other hand. In conclusion, Al and ML must continue, through multi-centric and validated research, to advance cardiovascular medicine, and overcome implementation challenges to boost patient outcomes and healthcare delivery.

Применение машинного обучения к данным нейроанатомии и физиологии в области диагностики СДВГ

&lt;p style="text-align: justify;"&gt;Attention Deficit\Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. While traditional diagnostic methods rely on clinical interviews, tests and behavioral observations, machine learning methods provide an opportunity to simplify the ADHD diagnostic process and make it more accurate. This review tries to explore the application of machine learning (ML) algorithms to physiological and neuroanatomical data: magnetic resonance imaging (MRI), functional MRI (fMRI), near-infrared spectroscopy (fNIRS), electroencephalography (EEG), magnetoencephalography (MEG), electrocardiogram (ECG), pupil parameters, eye tracking and activity in the field of exploring biomarkers for ADHD diagnosis. Deep learning models and support vector machines (SVM) are considered the most promising approaches for identifying ADHD in both children and adults. However, despite the fact that with the help of machine learning methods researchers are able to achieve high levels of specificity and sensitivity when solving problems of ADHD assessment, their use in clinical practice requires preliminary work to verify the results on large samples, as well as addressing data security and ethical issues.&lt;/p&gt;

Cardiac biomarkers as tools in the prediction and diagnosis of traumatic pericarditis and traumatic reticuloperitonitis in cattle and buffaloes

BackgroundIn the livestock industry, Foreign Body Syndrome is a devastating disease condition. Feeding management, lacking of food discrimination, and eating chopped food increase the risk of swallowing sharp foreign bodies in bovine species. In addition to the honeycomb cells shape of the reticulum, the contractions of the reticular wall, gravid uterine pressure, and parturition efforts, foreign bodies can penetrate the reticular wall, causing cascade of problems including traumatic reticulitis, traumatic reticuloperitonitis, and traumatic pericarditis. The present study was carried out to evaluate the diagnostic significance of cardiac troponin I rapid test cassette and other cardiac biomarkers including serum cardiac troponin I (cTn I), creatine kinase-myocardial band (CK-MB), lactate dehydrogenase (LDH), and aspartate aminotransferase enzyme (AST), in confirmed cases of traumatic pericarditis (TP) and/or traumatic reticuleoperitonitis (TRP) in cattle and buffaloes.MethodsA total number of 30 animals (22 cattle and 8 buffaloes) with different signs such as anorexia, jugular distension, brisket edema, and signs of pain (reluctance to move, arching back, and abduction of the forelimbs) were included in the present study. Based on case history, clinical signs, ferroscopic, pericardiocentesis, radiographic and ultrasonographic examinations, TP were confirmed in cattle (n = 10) and buffaloes (n = 8) while TRP were confirmed only in cattle (n = 12). Additionally, 20 clinically healthy animals (n = 10 cattle and 10 buffaloes) were used as a control group. Blood samples were collected for determination of blood level of Tn-I, and activity of CK-MB, LDH, and AST.ResultsThe obtained results revealed a highly significant increase in serum cTn I in diseased cattle with TP and TRP (P = 0.00), while buffaloes with TP showed no significant changes in serum cTn I (P = 0.111). Both diseased cattle and buffaloes showed increased serum activities of CK-MB, AST, and LDH enzyme. On the other hand, cardiac troponin I rapid test cassette failed to detect cTn I in diseased animals.ConclusionThe study concluded that the cardiac troponin I rapid test cassette did not have a diagnostic significance and could not be used as a point-of-care under field condition for diagnosis of TP and TRP in large ruminants. However, the serum troponin I level is helpful in diagnosis of TP and TRP in cattle. Although cardiac biomarkers have some diagnostic values in TP and TRP, the traditional diagnostic methods (clinical, radiography and ultrasonography examinations) are crucial for thorough evaluation of TP/TRP cases in bovine.

The Role of Selected Speech Signal Characteristics in Discriminating Unipolar and Bipolar Disorders.

The objective of this study is to explore and enhance the diagnostic process of unipolar and bipolar disorders. The primary focus is on leveraging automated processes to improve the accuracy and accessibility of diagnosis. The study aims to introduce an audio corpus collected from patients diagnosed with these disorders, annotated using the Clinical Global Impressions Scale (CGI) by psychiatrists. Traditional diagnostic methods rely on the clinician's expertise and consideration of co-existing mental disorders. However, this study proposes the implementation of automated processes in the diagnosis, providing quantitative measures and enabling prolonged observation of patients. The paper introduces a speech signal pipeline for CGI state classification, with a specific focus on selecting the most discriminative features. Acoustic features such as prosodies, MFCC, and LPC coefficients are examined in the study. The classification process utilizes common machine learning methods. The results of the study indicate promising outcomes for the automated diagnosis of bipolar and unipolar disorders using the proposed speech signal pipeline. The audio corpus annotated with CGI by psychiatrists achieved a classification accuracy of 95% for the two-class classification. For the four- and seven-class classifications, the results were 77.3% and 73%, respectively, demonstrating the potential of the developed method in distinguishing different states of the disorders.

The Role of Line-Field Confocal Optical Coherence Tomography in Detecting Extramammary Paget Disease Recurrences: A Pilot Diagnostic Study.

Extramammary Paget disease (EMPD) is an uncommon adenocarcinoma of apocrine gland-rich areas, presenting significant diagnostic challenges due to its nonspecific clinical appearance and frequent misidentification as benign, inflammatory skin conditions. Traditional diagnostic methods such as biopsy are invasive and uncomfortable, often required repeatedly due to high recurrence rates. Dermoscopy and non-invasive imaging techniques have been used but provide limited diagnostic accuracy due to their constraints in depth penetration and resolution. Recent advancements in imaging technologies, such as line-field confocal optical coherence tomography (LC-OCT), show promise in enhancing diagnostic precision while minimizing invasive procedures. LC-OCT merges high-resolution imaging with deep penetration capabilities, capturing detailed horizontal and vertical skin images akin to histopathology. This study evaluated the diagnostic performance of LC-OCT in detecting EMPD and its recurrence in 17 clinically suspicious anogenital regions, belonging to six patients. Data were collected prospectively at the patient's bedside by an LC-OCT expert with poor training for EMPD, and, then, reviewed retrospectively by an independent LC-OCT expert with adequate training for EMPD and no concerns about time. The prospective examination yielded 64.7% accuracy (11 true results out of 17 total cases), 71.4% sensitivity (10 true positives out of 14 actual positives), and 33.3% specificity (1 true negative out of 3 actual negatives). The retrospective analysis achieved 94.1% accuracy (16 true results out of 17 total cases), 100% sensitivity (14 true positives out of 14 actual positives), and 66.7% specificity (2 true positives out of 3 actual positives), with the only false positive case being a difficult-to-diagnose concomitant presentation of a lichen sclerosus et atrophicus. Despite the need for specialized training, our results suggest that LC-OCT represents a valuable tool for accurately identifying EMPD and improving its management by reducing unnecessary biopsies. Further studies are needed to standardize its clinical application.

Artificial intelligence in tuberculosis diagnosis: Revolutionizing detection and treatment

Artificial intelligence (AI) is rapidly transforming tuberculosis (TB) diagnosis. It is addressing the longstanding challenges in accuracy, efficiency, and accessibility. Traditional diagnostic methods, while effective, often suffer from limitations such as variability in sensitivity and lengthy turnaround times. AI technologies, including machine learning and deep learning algorithms, offer innovative solutions by automating the analysis of chest X-rays, genomic data, and clinical parameters. These advancements promise improved diagnostic accuracy, expedited treatment initiation, and personalized medicine approaches. However, successful implementation requires overcoming challenges related to data quality, integration with healthcare systems, and ethical considerations. Moving forward, this paper sheds light on AI-driven TB diagnosis, which stands poised to enhance global healthcare outcomes through enhanced detection capabilities and optimized treatment strategies.

Nanotechnological Advances in the Diagnosis of Gynecological Cancers and Nanotheranostics.

Gynecological cancers are one of the main causes of female mortality worldwide. Despite the various strategies to reduce mortality and improve quality of life, there are still many deficiencies in the diagnosis and treatment of gynecological cancers. One of the important steps to ensure optimal cancer treatment is the early detection of cancer cells and the use of drugs to reduce toxicity. Due to the increase in systemic toxicity and resistance to traditional and conventional diagnostic methods, new strategies, including nanotechnology, are being used to improve diagnosis and reduce the severity of the disease. Nanoparticles (NPs) provide exciting opportunities to improve Gynecological Cancers (GCs) diagnosis, particularly in the initial stages. In biomedical investigations and clinical settings, NPs can be used to increase the sensitivity and specificity of recognition and/or imaging of GCs with the help of their molecular and cellular processes. To design more efficient diagnostic NPs for gynecological cancer cells or tissues, determining the specific biomarkers is of great importance. NP-based imaging agents are another solution to trace cancer cells. This review highlights the potential of some NP-based diagnostic techniques in GC detection, which could be translated to clinical settings to improve patient care.

TF LITE BASED BLOOD CANCER CLASSIFICATION SYSTEM

Blood cancer, comprising various hematologic malignancies such as leukemia, lymphoma, and myeloma, presents significant diagnostic challenges that require precise and timely identification for effective treatment. The TF-Based Blood Cancer Classification System leverages the capabilities of TensorFlow (TF) to develop a robust, scalable, and accurate machine learning model for classifying different types of blood cancer from medical imaging and genetic data. This system integrates advanced deep learning techniques, including convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for genetic sequence analysis, to process and interpret complex biomedical data. The proposed system employs a comprehensive dataset containing labeled images and genetic profiles of various blood cancer types. Data preprocessing steps, such as normalization and augmentation, are applied to enhance model performance and generalization. The classification model is trained and validated using TensorFlow’s high- performance computing capabilities, achieving high accuracy and reliability in distinguishing between different blood cancer subtypes. Initial results demonstrate the system's potential to significantly improve diagnostic accuracy and speed, outperforming traditional diagnostic methods. This advancement not only aids healthcare professionals in making informed decisions but also contributes to personalized treatment strategies. The TF-Based Blood Cancer Classification System represents a significant step towards integrating artificial intelligence in medical diagnostics, offering promising prospects for improving patient outcomes in hematologic oncology. Keywords: Blood cancer, TensorFlow, machine learning, deep learning, convolutional neural networks, recurrent neural networks, medical imaging, genetic data, diagnostics, hematologic malignancies.

Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning

aims: This study aims to leverage artificial intelligence for enhancing medical diagnosis, focusing on ultrasound evaluation of fetal development and detection of fetal diseases. background: Traditional diagnostic methods in ultrasound are known for being time-consuming and laborious, prompting the need for more efficient approaches. objective: The objective of this research is to develop an end-to-end automatic diagnosis system using convolutional neural networks with ensemble learning to enhance robustness and accuracy in classifying ultrasound images. method: The study involves constructing and implementing the automatic diagnosis system, training it on a diverse dataset encompassing six categories: abdomen, brain, femur, thorax, maternal cervix, and other planes. result: Experimental results demonstrate that the proposed end-to-end system significantly improves the detection accuracy of the standard plane in ultrasound images. conclusion: The application of artificial intelligence through an ensemble learning-based automatic diagnosis system shows promise in advancing ultrasound-based medical diagnosis, particularly in fetal development assessment. other: This research contributes to the ongoing efforts in leveraging technology for more efficient and accurate medical diagnostic processes.

Navigating the Modern Landscape of Sepsis: Advances in Diagnosis and Treatment

Sepsis poses a significant threat to human health due to its high morbidity and mortality rates worldwide. Traditional diagnostic methods for identifying sepsis or its causative organisms are time-consuming and contribute to a high mortality rate. Biomarkers have been developed to overcome these limitations and are currently used for sepsis diagnosis, prognosis prediction, and treatment response assessment. Over the past few decades, more than 250 biomarkers have been identified, a few of which have been used in clinical decision-making. Consistent with the limitations of diagnosing sepsis, there is currently no specific treatment for sepsis. Currently, the general treatment for sepsis is conservative and includes timely antibiotic use and hemodynamic support. When planning sepsis-specific treatment, it is important to select the most suitable patient, considering the heterogeneous nature of sepsis. This comprehensive review summarizes current and evolving biomarkers and therapeutic approaches for sepsis.

Navigating the Modern Landscape of Sepsis: Advances in Diagnosis and Treatment.

Sepsis poses a significant threat to human health due to its high morbidity and mortality rates worldwide. Traditional diagnostic methods for identifying sepsis or its causative organisms are time-consuming and contribute to a high mortality rate. Biomarkers have been developed to overcome these limitations and are currently used for sepsis diagnosis, prognosis prediction, and treatment response assessment. Over the past few decades, more than 250 biomarkers have been identified, a few of which have been used in clinical decision-making. Consistent with the limitations of diagnosing sepsis, there is currently no specific treatment for sepsis. Currently, the general treatment for sepsis is conservative and includes timely antibiotic use and hemodynamic support. When planning sepsis-specific treatment, it is important to select the most suitable patient, considering the heterogeneous nature of sepsis. This comprehensive review summarizes current and evolving biomarkers and therapeutic approaches for sepsis.