Diagnosis Capability Research Articles

Automatic statistical diagnosis of COVID-19 based on multi-modal CT feature extraction

Background and purposeComputed tomography (CT) is highly sensitive to lung-related abnormalities as a non-invasive method and has become an essential tool for screening and diagnosing Coronavirus disease 2019 (COVID-19). To reduce the stress of work for physicians and speed up diagnosis, we propose a novel automatic diagnosis pipeline for COVID-19 based on high-dimensional radiomic features extracted from multimodal CT scans (multi-geometric and multiscale). Materials and methodsThere are 746 CT scans involved in this study, where 349 CT scans are COVID-19 positive and 397 CT scans are COVID-19 negative. All of them are from the public dataset. We first construct a transfer learning-based auto-segmentation model with a morphological post-processing block to improve the lung region segmentation. Then the radiomics feature extraction is guided by the proposed multi-modal CT scans strategy. In addition, our automatic diagnosis pipeline is driven by a well-designed loss function. We also explain the diagnosis capability from the related theory of linear subspace spanned by multi-modal radiomics features. ResultsUnder the 10-fold cross-validation strategy, our approach can achieve an improvement in diagnostic performance of 5. 77%, 7. 78%, 7. 74%, 7. 78%, 7. 45% compared to the radiomic features extracted from the original CT scans, and diagnosis performance is promoted to 91.53%, 86.46%, 86.47%, 86.46%, 86.95% in terms of AUC, Acc, F1, Recall and Precision in public datasets. ConclusionsWe demonstrate a statistically significant improvement of the proposed statistical learning method compared to the state-of-the-art machine learning-based diagnosis approaches. Thanks to theoretical support and excellent diagnostic performance, our method can be deployed in clinical auxiliary diagnosis, releasing the overstretched medical resources.

Quadruple-Rule-Out Computed Tomography Angiography (QRO-CT): A Novel Dual-Energy Computed Tomography Technique for the Diagnostic Work-Up of Acute Chest Pain.

Computerized tomography (CT) has been increasingly utilized in the differential diagnosis of acute chest pain. Combining the triple rule out CT angiography (TRO-CT) approach with dual-energy CT (DECT) can enhance the diagnostic capability by identifying myocardial perfusion deficiencies. This combination can yield a quadruple-rule-out computed tomography angiography (QRO-CT) technique. The aim of this study is to determine the efficacy of the QRO-CT. Intraluminal diseases and abnormalities in the main coronary arteries and branches were investigated. The myocardial dark spots on the color-coded iodine map were identified as perfusion deficiencies. Pulmonary arteries and aorta were also evaluated. The study population consisted of 211 patients. The sensitivity, specificity, and positive and negative predictive values of QRO-CT for pulmonary embolism were 93.5%, 100%, 100%, and 95.3%, respectively. For obstructive coronary artery disease, the values were 96.1%, 93.4%, 89.2%, and 97.7%, respectively. For myocarditis, the values were 69.2%, 100%, 100%, and 93.6%, respectively. the QRO-CT method may successfully evaluate myocardial perfusion deficits, hence expanding the differential diagnosis capabilities of the standard TRO-CT method for myocarditis. It can provide useful information on myocardial perfusion, which may influence the choice to perform invasive catheterization in cases of coronary artery obstruction.

A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms.

Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity.

Modern capabilities of lifetime morphological diagnosis of mycobacterial defeat

Modern capabilities of lifetime morphological diagnosis of mycobacterial defeat

Leptospira Status in Sweden during the Past Century, Neglected and Re-Emerging?

We compiled data on notified cases of leptospirosis in animals and humans in Sweden. Published studies on leptospirosis in humans and animals from the beginning of the 20th century onwards are summarized. During the Second World War, hundreds of leptospirosis cases in humans were reported in Sweden, but since then, there have been only a few severe cases. Surveillance of leptospirosis in domestic animals demonstrates that the pathogen is still occurring. The occurrence of Leptospira in humans and animals in the other Nordic countries resembles that in Sweden. Leptospirosis is an underdiagnosed and underreported disease globally, both in animals and humans, partly due to the lack of simple, rapid diagnostic tools but possibly also due to the lack of awareness among physicians, veterinarians and nurses. Traditionally, leptospirosis has been mostly diagnosed by serology, but development of molecular methodshas improved the capability for correct diagnosis. As of today, leptospirosis is regarded as a relatively uncommon disease in the Nordic countries, but in some other countries, it is considered a neglected zoonosis or a (re-)emerging disease that may become more common in the future. Possible factors that could contribute to an increase in incidence are discussed in this review. Active surveillance of humans and domestic and wild animals and stringent rodent control in society and animal farms are of outmost importance for prevention.

Bayesian bi-level variable selection for genome-wide survival study.

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

Open-set federated adversarial domain adaptation based cross-domain fault diagnosis

Abstract Data-driven fault diagnosis techniques utilizing deep learning have achieved widespread success. However, their diagnostic capability and application possibility are significantly reduced in real-world scenarios where fault modes are not fully covered and labels are lacking. Owing to potential conflicts of interest and legal risks, industrial equipment fault data usually exist in the form of isolated islands, making it difficult to carry out large-scale centralized model training. This paper proposes open-set federated adversarial domain adaptation (OS-FADA) to achieve collaborative evolution of fault diagnosis capabilities among cross-domain data owners while protecting privacy. The OS-FADA is a general fault diagnosis framework that employs two-phase adversarial learning. First, faced with the data distribution shift caused by variable working conditions, a generative adversarial feature extractor training strategy is designed to achieve domain-invariant fault feature extraction by approximating the feature distributions of clients to a unified generated distribution. Second, considering the label distribution shift of unknown faults occurring in the target client, an adversarial learning method is proposed to establish decision boundaries between known and unknown faults. Ultimately, the co-evolution of fault diagnosis models between clients is achieved by combining two-phase adversarial learning and federated aggregation. Results from an industrial gearbox case demonstrate that our proposed method achieves over 20% diagnostic accuracy improvement and has excellent potential for cross-domain fault diagnosis tasks with unknown faults when the data silos problem cannot be ignored.

A blockchain database system for a rail transit equipment system

AbstractAs of today, blockchain technology is virtually absent from rail transit applications, however, it has promising potential. For cloud‐based systems or conventional systems, there is too much information at the centre, resulting in a high level of data processing. However, if the data processing capability can be provided at the edge station or at the edge equipment, it greatly reduces the operation and maintenance pressure, at the same time, if the edge device takes the form of a blockchain, it is possible to increase the safety protection capability. It is important to reduce operating and maintenance risks for metro equipment systems by installing fault diagnosis and health management capabilities. This paper presents a blockchain‐based platform for subway applications. The assistance includes design, construction, operation, and maintenance assistance, energy saving assistance, manpower reduction assistance, fire protection linkage assistance at transfer stations, federal learning analysis and artificial intelligence analysis, as well as specific implementation plans for implementing vent valve equipment as examples.

Towards Efficient and Trustworthy Pandemic Diagnosis in Smart Cities: A Blockchain-Based Federated Learning Approach

In the aftermath of the COVID-19 pandemic, the need for efficient and reliable disease diagnosis in smart cities has become increasingly serious. In this study, we introduce a novel blockchain-based federated learning framework tailored specifically for the diagnosis of pandemic diseases in smart cities, called BFLPD, with a focus on COVID-19 as a case study. The proposed BFLPD takes advantage of the decentralized nature of blockchain technology to design collaborative intelligence for automated diagnosis without violating trustworthiness metrics, such as privacy, security, and data sharing, which are encountered in healthcare systems of smart cities. Cheon–Kim–Kim–Song (CKKS) encryption is intelligently redesigned in BFLPD to ensure the secure sharing of learning updates during the training process. The proposed BFLPD presents a decentralized secure aggregation method that safeguards the integrity of the global model against adversarial attacks, thereby improving the overall efficiency and trustworthiness of our system. Extensive experiments and evaluations using a case study of COVID-19 ultrasound data demonstrate that BFLPD can reliably improve diagnostic accuracy while preserving data privacy, making it a promising tool with which smart cities can enhance their pandemic disease diagnosis capabilities.

The Epidemic Characteristics of Stroke Death from 2012 to 2021 in Chongqing, China.

Stroke has become a major disease that threatens the global population's health and is a major public health problem that needs to be solved in China. Therefore, it is essential to analyze the trend of the mortality of stroke and its epidemic characteristic of stroke death. Death cases of stroke were reported to the national death registry system by the medical staff of all medical institutions, and the population data every year were obtained from District or County's Statistic Bureau in Chongqing. They were analyzed to calculate the mortality, age-standardized mortality rate by Chinese standardization population (ASMRC), age-specific mortality, proportion, and annual percent of change (APC) according to the ICD-10 code. ASMRC was based on the standard population of the 6th census in China, 2010. The stroke mortality of each subgroup was compared using the χ2 test. Trend analysis was presented by APC. The crude mortality of stroke increased from 96.29 per 100,000 in 2012 to 115.93 per 100,000 significantly, with the APC of 2.02% (t = 2.82, p = 0.022) in Chongqing. ASMRC of stroke was 56.47 per 100,000 in 2012 and 54.70 per 100,000 in 2021, and its trend change was stable (APC = -0.01, t = 0.07, p = 0.947). The crude mortality of stroke in males was higher than that in females every year (p < 0.05). The death proportion of intracerebral hemorrhage dwindled from 60.53% in 2012 to 49.88% in 2021, whereas the death proportion of ischemic stroke increased from 20.92% in 2012 to 39.96% in 2021. The average age of stroke death was delayed from 73.43 years old in 2012 to 76.52 years old in 2021 significantly (t = 18.12, p < 0.001). The percentage of stroke death at home increased from 75.23% in 2012 to 79.23% in 2021, while the percentage of stroke death at hospitals decreased from 17.89% in 2012 to 15.89% in 2021. The crude mortality of stroke surged, and intracerebral hemorrhage was the main death cause of all subtypes. The mortality of stroke in males and rural residents was higher than that in females and urban residents. Most stroke deaths occurred at home. Male and rural residents were crucial populations for stroke prevention and control. There should be improved medical resources in rural areas and enhanced capability of stroke diagnosis and treatment.

AcneTyper: An automatic diagnosis method of dermoscopic acne image via self-ensemble and stacking.

Acne is a skin lesion type widely existing in adolescents, and poses computational challenges for automatic diagnosis. Computer vision algorithms are utilized to detect and determine different subtypes of acne. Most of the existing acne detection algorithms are based on the facial natural images, which carry noisy factors like illuminations. In order to tackle this issue, this study collected a dataset ACNEDer of dermoscopic acne images with annotations. Deep learning methods have demonstrated powerful capabilities in automatic acne diagnosis, and they usually release the training epoch with the best performance as the delivered model. This study proposes a novel self-ensemble and stacking-based framework AcneTyper for diagnosing the acne subtypes. Instead of delivering the best epoch, AcneTyper consolidates the prediction results of all training epochs as the latent features and stacks the best subset of these latent features for distinguishing different acne subtypes. The proposed AcneTyper framework achieves a promising detection performance of acne subtypes and even outperforms a clinical dermatologist with two-year experiences by 6.8% in accuracy. The method we proposed is used to determine different subtypes of acne and outperforms inexperienced dermatologists and contributes to reducing the probability of misdiagnosis.

Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network

Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical equipment. In response to the problem of frequent faults in rolling bearings, this paper designs a rolling bearing fault diagnosis method based on Convolutional Capsule Network (CCN). More specifically, the original vibration signal is converted into a two-dimensional time-frequency image using continuous wavelet transform (CWT), and the feature extraction is performed on the two-dimensional time-frequency image using the convolution layer at the front end of the network, and the extracted features are input into the capsule network. The capsule network converts the extracted features into vector neurons, and the dynamic routing algorithm is used to achieve feature transfer and output the results of fault diagnosis. Two different datasets are used to compare with other traditional deep learning models to verify the fault diagnosis capability of the method. The results show that the convolutional capsule network has good diagnostic capability under different working conditions, even in the presence of noise and insufficient samples, compared to other models. This method contributes to the safe and reliable operation of mechanical equipment and is suitable for other rotating scenarios.

A New Algorithm of Cubic Dynamic Uncertain Causality Graph for Speeding Up Temporal Causality Inference in Fault Diagnosis

Representing and reasoning uncertain causalities have diverse applications in fault diagnosis for industrial systems. Owing to the complicated dynamics and a multitude of uncertain factors in such systems, it is hard to implement efficient diagnostic inference when a process fault occurs. The cubic dynamic uncertain causal graph was proposed for graphically modeling and reasoning about the fault spreading behaviors in the form of causal dependencies across multivariate time series. However, in certain large-scale scenarios with multiconnected and time-varying causalities, the existing inference algorithm is incapable of dealing with the logical reasoning process in an efficient manner. We, therefore, explore the solutions to enhanced computational efficiency. Causality graph decomposition and simplification, and graphical transformation, are proposed to reduce model complexity and form a minimal causality graph. An algorithm, event-oriented early logical absorption, is also presented for logical reasoning. It is mainly intended to minimize the computational costs for compulsory absorption operations in the early stage of reasoning process. The effectiveness of the proposed algorithm is verified on the secondary loop model of nuclear power plant by utilizing the fault data derived from a nuclear power plant simulator. The results show the anticipated capability of efficient fault diagnosis of the proposed algorithm for large-scale dynamic systems.

New living evidence resource of human and non-human studies for early intervention and research prioritisation in anxiety, depression and psychosis

In anxiety, depression and psychosis, there has been frustratingly slow progress in developing novel therapies that make a substantial difference in practice, as well as in predicting which treatments will...

New possibilities in determining the individual norm of intraocular pressure

This article presents the preliminary results of a clinical trial of the Individual Intraocular Pressure (IOP) Norm Analyzer. The screening method for determination of individual norm of IOP proved the efficiency for early glaucoma diagnosis, as well as in the treatment and monitoring of the disease. Patients with IOP elevated for up to 15% relative to tolerant IOP were put into the group with low risk of disease development, with IOP elevation of 15 to 25% — the group with average risk, by more than 25% — the group with high risk of developing glaucoma. The Individual IOP Norm Analyzer is an effective device for dynamic monitoring, which in combination with other examination methods increases the capabilities of early diagnosis, monitoring of glaucoma with regard to individual parameters.

Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review.

Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.

Integrating MWCNTs-doped MXene with multi-spiral-channel architecture enables field effect transistor biosensor capable of ultrasensitive determination of methotrexate

Previous breakthroughs in biosensor diagnostics stem from engineering and nanocomposites. Accurately detecting low-abundance compounds such as methotrexate in complex biospecimens (e.g. serum) is an important clinical challenge. To address this issue, a MWCNTs-doped MXene-based multi-spiral-channel field-effect transistor (MMSFETs) biosensor was constructed for ultrasensitive quantification of methotrexate. Our integrated biosensor exhibited following merits: a) The synergetic performance of MXene and MWCNTs for enhanced transconductance (0.63 mS) and detection capability (methotrexate, linear range of 0.001–100 μM and LOD down to 0.352 nM); b) Favorable selectivity, stability (one month), reproducibility (RSD = 0.99%, n = 7) for biosensing of methotrexate; c) Acceptable clinical performances on comparisons of MMFETs against commercial Abbott automatic immunoluminescence instrument (ARCHITECT I1000): favorable linearity and correlation coefficient (YMMSFETs = 1.4305 × Xtargeted concentration + 4.3791 with R2 = 0.949), significant p value (7.68E-12 < 0.001) and diagnosis capability of AUC (0.9907). Those advantages are anticipated to pave an avenue to design of the FETs-based biosensor towards the point-of-care diagnostics applications.

Visceral leishmaniasis in Xinjiang Uygur Autonomous Region during the COVID-19 pandemic: a case report

To perform an epidemiological investigation on a case of visceral leishmaniasis reported from Shule County, Kashi Prefecture, Xinjiang Uygur Autonomous Region in 2021, so as to provide insights into differential diagnosis of visceral leishmaniasis during the COVID-19 pandemic. The epidemiological history of this case was collected, and the case was diagnosed for Leishmania infection with the immunochromatographic (rK39) strip test, bone marrow smear microscopy and PCR assay. The patient had typical clinical symptoms of leishmaniasis, including irregular fever, hepatosplenomeg- aly, low serum albumin and elevated globulin. Bone marrow smear microscopy identified L. donovani amastigotes, and both rK39 strip test and PCR assay were positive, while the case was tested negative for SARS-CoV-2. COVID-19 was therefore excluded and visceral leishmaniasis was diagnosed. Standard full-dose treatment with sodium stibogluconate was given, and no Leishmania was found on blood smears during the reexamination. No recurrence was found during the followup after discharge for hospital. During the COVID-19 pandemic, it is recommended to increase the perception of differential diagnosis of visceral leishmaniasis among first-contact doctors, and reinforce the capability of differential diagnosis and health education of visceral leishmaniasis among medical and healthcare institutions at all levels, to prevent missed diagnosis and misdiagnosis of visceral leishmaniasis.

Near-infrared-II-activatable sulfur-deficient plasmonic Bi2S3-x-Au heterostructures for photoacoustic imaging-guided ultrasound enhanced high performance phototherapy

Bismuth sulfide is widely used as an n-type semiconductor material in photocatalytic reactions. However, bismuth sulfide has poor absorption in the near-infrared region and low charge separation efficiency, limiting its application in phototherapy and sonodynamic therapy (SDT). In this study, we successfully synthesized an “all-in-one” phototheranostic nanoplatform, namely Bi2S3-x-Au@HA, based on a single second near-infrared (NIR-II) light-responsive Schottky-type Bi2S3-x-Au heterostructure for photoacoustic (PA) imaging-guided SDT-enhanced photodynamic therapy (PDT)/photothermal therapy (PTT). Bi2S3-x-Au@HA exhibits excellent NIR-II plasmonic and photothermal properties, rendering it with NIR-II PA imaging capabilities for accurate diagnosis. Additionally, the high-density sulfur vacancies constructed on the Bi2S3 surface cause it to possess a reduced band gap (1.21 eV) that can act as an electron trap. Using the density functional theory, we confirmed that the light and ultrasound-induced electrons are more likely to aggregate on the Au nanoparticle surface through interfacial self-assembly, which promotes electron-hole separation and enhances photocatalytic activity with increased reactive oxygen species (ROS) generation. With a further modification of hyaluronic acid (HA), Bi2S3-x-Au@HA can selectively target cancer cells through HA and CD44 protein interactions. Both in vitro and in vivo experiments demonstrated that Bi2S3-x-Au@HA effectively suppressed tumor growth through SDT-enhanced PTT/PDT under a single NIR-II laser and ultrasound irradiation with negligible toxicity. Our findings provide a framework for fabricating Schottky-type heterostructures as single NIR-II light-responsive nanotheranostic agents for PA imaging-guided cancer phototherapy.

Standardize and enhance the diagnosis and treatment capabilities of chronic lymphoblastic leukemia in China

Standardize and enhance the diagnosis and treatment capabilities of chronic lymphoblastic leukemia in China