Accuracy Of Disease Diagnosis Research Articles

Fuzzy inference rule based image despeckling using adaptive maximum likelihood estimation

Despite multitude of research ponders on despeckling ultrasound images, contriving an efficient despeckling method still exist as an open challenge. The presence of speckle noise in ultrasound images complicates the accuracy of disease diagnosis. The classical Rayleigh Maximum Likelihood Estimation (RMLE) based despeckling filter was exclusively proposed to despeckle ultrasound images. It provides proper tradeoff between speckle suppression and edge preservation by discriminating the image region as edge and background correspondingly. Many despeckling filters utilize various statistical measures which remains uncertain in order to distinguish between image edge region and background region. This proposed filter harness the Fuzzy inference rules based image connectivity measure to avoid ambiguity in image region discrimination and adapts an appropriate tuning parameter to modify classical RMLE despeckling. The proposed method involves three steps in which the first step uses Fuzzy inference rules to categorize the type of image area as edge and background. Second step involves recursive optimal selection of appropriate filter tuning parameter by using adaptive technique. Third step involves estimation of noise-free pixels by using RMLE formulation. Quantitative evaluation was made by considering various performance metrics to compare the proposed filter and existing filters. The obtained result demonstrates that the proposed filter predominantly preserves the edges structures and clinical features.

Identifying joint biomarker panel from multiple level dataset by an optimization model.

Joint biomarker panel takes advantage of coherence across multiple level datasets and holds the promise to improve disease diagnosis accuracy. We collected 101 colorectal cancer and 95 benign samples, measured the molecular concentrations by serum assays and mass spectra, and developed LPGLO (Linear Programming based on Group Lasso Optimization) to identify the joint biomarker panel. A joint biomarker panel, with ten serum biomarkers and six mass spectra peaks, achieves LOOCV accuracy 0.8724, which is better than the optimal panels identified from separate datasets (LOOCV = 0.7551 for mass spectra only or 0.8265 for serum assay only) and the simply merged dataset (LOOCV = 0.8622). LPGLO is useful to identify joint biomarker panel to help tumor diagnosis.

Advances in multifunctional nano-sized CT contrast agents

Computed tomography (CT) is one of the most widely used noninvasive clinical imaging modalities because of its wide availability, low cost, high efficiency and high resolution. Although the resolution of CT is much higher than that of conventional radiography and many other imaging techniques, it is still difficult to distinguish subtle changes of soft tissues because of physical limitations. To increase the contrast of soft tissues and improve the accuracy of disease diagnosis, a variety of CT contrast agents have been developed. Small iodinated compounds have been widely used as clinical CT contrast agents, but their toxicity and short circulation time restrict their further application. To resolve these problems, nanosized CT contrast agents have been developed. In addition to iodine, nanoparticles based on heavy atoms such as gold, tantalum, lanthanides, and bismuth have been investigated as more efficient CT contrast agents than small iodinated compounds. These novel nanosized CT contrast agents have high biocompatibility and long circulation times, which are advantageous for visualization of blood vessels and tumors. Moreover, the combination of CT with other imaging techniques, such as magnetic resonance imaging and ultrasound imaging, could realize higher sensitivity and more personalized diagnostic information. In particular, theranostic CT contrast agents that integrate diagnosis with therapy in a single nanoplatform may serve as a nanomedicine capable of both noninvasive imaging and remote-controlled therapy. Herein, we introduce the principles of CT imaging and commonly used nanoscale CT contrast elements, including iodine, gold, bismuth, tantalum, and lanthanides. The recent advances, future development and existing problems of multifunctional nanosized CT contrast agents are reviewed.

BODIPY: A Highly Versatile Platform for the Design of Bimodal Imaging Probes.

In molecular imaging, multimodal imaging agents can provide complementary information, for improving the accuracy of disease diagnosis or enhancing patient management. In particular, optical/nuclear imaging may find important preclinical and clinical applications. To simplify the preparation of dual-labeled imaging agents, we prepared versatile monomolecular multimodal imaging probe (MOMIP) platforms containing both a fluorescent dye (BODIPY) and a metal chelator (polyazamacrocycle). One of the MOMIP was conjugated to a cyclopeptide (i.e., octreotide) and radiolabeled with (111) In. In vitro and in vivo studies of the resulting bioconjugate were conducted, highlighting the potential of these BODIPY-based bimodal probes. This work also confirmed that the biovector and/or the bimodal probes must be chosen carefully, due to the impact of the MOMIP on the overall properties of the resulting imaging agent.

A Hybrid Particle Swarm Optimization and Neural Network with Fuzzy Membership Function Technique for Epileptic Seizure Classification

A hybrid particle swarm optimization (PSO) integrating neural network with fuzzy membership function (NEWFM) technique is proposed for epileptic seizure classification tasks based on brain electroencephalography (EEG) signals. By combining PSO and NEWFM, the proposed method obtains the optimal parameters from the EEG data training required to achieve the best accuracy in disease diagnosis. NEWFM, a model of neural networks, is expected to improve the accuracy by updating weights of fuzzy membership functions. The PSO, a swarm-inspired optimization algorithm, is used to obtain the optimal parameters from the NEWFM. A standard dataset comprising of 5 sets of epileptic seizure detection data, each consisting 100 single EEGs segments is employed to evaluate the proposed technique’s performance. Based on the experiments, the classification results show that the best accuracy of Z–S classification task is 99.5% with the optimal parameters of α = 0.1 and β=0.1. For the ZNF–S classification task, the best accuracy is 97.73% with the optimal parameters of α=0.1 or 0.2 and β =0.2. Similar results for the ZNFO–S classification task is 97.64% with the optimal parameters set at α =0.1 or 0.2 and β = 0.1.

DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations.

Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease–disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random walk prioritization in a protein–protein interaction network. This approach considers not only whether two diseases directly share associated genes but also the statistical relationships between two different diseases using known disease-related genes. Predicted associations were validated by known DDAs from a database and literature supports. The method yielded a good performance with an area under the curve of 71% and outperformed other standard association indices. Furthermore, novel DDAs and relationships among diseases from the clusters analysis were reported. This method is efficient to identify disease–disease relationships on an interaction network and can also be generalized to other association studies to further enhance knowledge in medical studies.

Design of the Pulse Wave Digital Filters Based on MATLAB

Quality of human pulse waveform graph determine the results of the disease analysis and affect disease diagnosis accuracy，Through the MATLAB software design of a digital filter is used to filter the pulse signals, Optimizing the pulse waveform graph. And the simulation test of digital filter, results showed that filter completed the pulse wave denoising.

Functional molecular units for guiding biomarker panel design.

The field of biomarker research has experienced a major boost in recent years, and the number of publications on biomarker studies evaluating given, but also proposing novel biomarker candidates is increasing rapidly for numerous clinically relevant disease areas. However, individual markers often lack sensitivity and specificity in the clinical context, resting essentially on the intra-individual phenotype variability hampering sensitivity, or on assessing more general processes downstream of the causative molecular events characterizing a disease term, in consequence impairing disease specificity. The trend to circumvent these shortcomings goes towards utilizing multimarker panels, thus combining the strength of individual markers to further enhance performance regarding both sensitivity and specificity. A way of identifying the optimal composition of individual markers in a panel approach is to pick each marker as representative for a specific pathophysiological (mechanistic) process relevant for the disease under investigation, hence resulting in a multimarker panel for covering the set of pathophysiological processes underlying the frequently multifactorial composition of a clinical phenotype.Here we outline a procedure of identifying such sets of disease-specific pathophysiological processes (units) delineated on the basis of disease-associated molecular feature lists derived from literature mining as well as aggregated, publicly available Omics profiling experiments. With such molecular units in hand, providing an improved reflection of a specific clinical phenotype, biomarker candidates can then be assigned to or novel candidates are to be selected from these units, subsequently resulting in a multimarker panel promising improved accuracy in disease diagnosis as well as prognosis.

Simple Patterned Nanofiber Scaffolds and Its Enhanced Performance in Immunoassay

Cancer has become the leading cause of death worldwide; early diagnosis and treatment of cancers is critical for the survival of the patients. The concentration of cancer markers in easy-to-access biological fluids can provide great assistance in screening for occult primary cancers, distinguishing malignant from benign findings, determining prognosis and prediction for cancer patients. The multiplex detection technology of a panel of cancer markers can greatly increase the accuracy of disease diagnosis. Herein, we briefly fabricate a high-throughput micro-immunoassay based on the electrospun polystyrene (PS) substrates to improve detection sensitivity. The immunoassay was evaluated by analyzing three different cancer biomarkers (AFP, CEA, VEGF). For AFP, CEA, VEGF immunofluorescence assay, the LOD of assay conducted on electrospun PS substrates before or after plasma and the conventional PS substrates were 0.42, 0.10, 1.12 ng/mL, 0.57, 0.09, 1.24 ng/mL, and 159.75, 26.19, 385.59 pg/mL, respectively (P < 0.05). Due to the high porosity and large surface area-to-volume ratio which is the foremost merit of nanostructures, and the plasma treatment which make the hydrophobic PS nanofibers hydropholic, the nanofibers substrates showed sufficient retention of immunoassay functionality and high potential for capture molecules immobilization. Consequently, the immunofluorescence assay conducted on electrospun PS substrates could significantly enhance the sensitivity and limits of detection.

Automatic Diagnosis of Pathological Myopia from Heterogeneous Biomedical Data

Pathological myopia is one of the leading causes of blindness worldwide. The condition is particularly prevalent in Asia. Unlike myopia, pathological myopia is accompanied by degenerative changes in the retina, which if left untreated can lead to irrecoverable vision loss. The accurate diagnosis of pathological myopia will enable timely intervention and facilitate better disease management to slow down the progression of the disease. Current methods of assessment typically consider only one type of data, such as that from retinal imaging. However, different kinds of data, including that of genetic, demographic and clinical information, may contain different and independent information, which can provide different perspectives on the visually observable, genetic or environmental mechanisms for the disease. The combination of these potentially complementary pieces of information can enhance the understanding of the disease, providing a holistic appreciation of the multiple risks factors as well as improving the detection outcomes. In this study, we propose a computer-aided diagnosis framework for Pathological Myopia diagnosis through Biomedical and Image Informatics(PM-BMII). Through the use of multiple kernel learning (MKL) methods, PM-BMII intelligently fuses heterogeneous biomedical information to improve the accuracy of disease diagnosis. Data from 2,258 subjects of a population-based study, in which demographic and clinical information, retinal fundus imaging data and genotyping data were collected, are used to evaluate the proposed framework. The experimental results show that PM-BMII achieves an AUC of 0.888, outperforming the detection results from the use of demographic and clinical information 0.607 (increase , ), genotyping data 0.774 (increase , ) or imaging data 0.852 (increase , ) alone. The accuracy of the results obtained demonstrates the feasibility of using heterogeneous data for improved disease diagnosis through our proposed PM-BMII framework.

Value of autopsy in Internal Medicine: discrepancies between clinical and postmortem diagnosis

Introduction The number of autoptic controls is progressively dropping worldwide during the last decades. Several social, cultural and normative causes may underlie this phenomenon. However, a hidden cause may be represented by the scepticism of physicians about the gain in information given by this practice. There is in fact a general thinking that available diagnostic techniques may be adequate for a correct diagnosis, even in the case of a mortal outcome. Nevertheless, several studies have shown quite relevant discrepancies between premortem and postmortem diagnosis. In this study, the authors evaluate the accuracy of clinical diagnosis in diseases leading the patient to death.Materials and methods Retrospective analysis including all patients died in the Department of Internal Medicine of a General Hospital in Rome during a three year time period (1st January 2007–31 December 2009). Age, sex, period of hospitalization, clinical diagnosis, autoptic diagnosis, cause of death, and level of discrepancy between clinical diagnosis and autoptic control have been collected. Main diagnoses have been classified as follows: (1) discrepancy with a possible influence on survival; (2) discrepancy with no or questionable influence on survival.Results Ninety-two cases (42 males, 50 females; mean age 79.3 years) have been included. Thirty-four main diagnoses (36.9%) have been classified as discrepant (15.2% classified as type 1 discrepancy and 21.7 as type 2).DiscussionA substantial discrepancy between clinical diagnosis and autoptic control is confirmed by the current study, which supports the role of autopsy as a tool for the improvement of medical practice.

Proteomic pattern analysis discriminates among multiple sclerosis–related disorders

To use a new, unbiased biomarker discovery strategy to obtain and assess proteomic data from cerebrospinal fluid (CSF) of patients with multiple sclerosis (MS)-related disorders. CSF protein profiles were analyzed from 107 patients with either MS-related disorders (including relapsing remitting MS [RRMS], primary progressive MS [PPMS], anti-aquaporin4 antibody seropositive-neuromyelitis optica spectrum disorder [SP-NMOSD], and seronegative-NMOSD with long cord lesions on spinal magnetic resonance imaging [SN-NMOSD]), amyotrophic lateral sclerosis (ALS), or other inflammatory neurological diseases (used as controls). CSF peptides/proteins were purified with magnetic beads, and directly measured by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The obtained spectra were analyzed with multivariate statistics and pattern matching algorithms. These analyses were replicated in an independent sample set of 84 patients composed of those with MS-related disorders or with other neurological diseases (the second cohort). MS-related disorders differed considerably in terms of CSF protein profiles. SP-NMOSD and SN-NMOSD, both of which fit within the NMO spectrum, were distinguishable from RRMS with high cross-validation accuracy on a support vector machine classifier, especially in relapse phases. Some peaks derived from samples of relapsed SP-NMOSD can discriminate RRMS with high area under curve scores (>0.95) and this was reproduced on the second cohort. The similarity of proteomic patterns between selected neurological diseases were demonstrated by pattern matching analysis. To our surprise, the spectral differences between RRMS and PPMS were much larger than those of PPMS and ALS. Our findings suggest that CSF proteomic pattern analysis can increase the accuracy of disease diagnosis of MS-related disorders and will aid physicians in appropriate therapeutic decision-making.

Unsupervised Clustering of Multivariate Time Series Microarray Experiments based on Incremental Non-Gaussian Analysis

Multiple expression levels of genes obtained using time series microarray experiments have been exploited effectively to enhance understanding of a wide range of biological phenomena. However, the unique nature of microarray data is usually in the form of large matrices of expression genes with high dimensions. Among the huge number of genes presented in microarrays, only a small number of genes are expected to be effective for performing a certain task. Hence, discounting the majority of unaffected genes is the crucial goal of gene selection to improve accuracy for disease diagnosis. In this paper, a non-Gaussian weight matrix obtained from an incremental model is proposed to extract useful features of multivariate time series microarrays. The proposed method can automatically identify a small number of significant features via discovering hidden variables from a huge number of features. An unsupervised hierarchical clustering representative is then taken to evaluate the effectiveness of the proposed methodology. The proposed method achieves promising results based on predictive accuracy of clustering compared to existing methods of analysis. Furthermore, the proposed method offers a robust approach with low memory and computation costs.

Utilità dell’autopsia in Medicina Interna: discrepanze tra diagnosi clinica e diagnosi autoptica

The number of autoptic controls is progressively dropping worldwide during the last decades. Several social, cultural and normative causes may underlie this phenomenon. However, a hidden cause may be represented by the scepticism of physicians about the gain in information given by this practice. There is in fact a general thinking that available diagnostic techniques may be adequate for a correct diagnosis, even in the case of a mortal outcome. Nevertheless, several studies have shown quite relevant discrepancies between premortem and postmortem diagnosis. In this study, the authors evaluate the accuracy of clinical diagnosis in diseases leading the patient to death. Retrospective analysis including all patients died in the Department of Internal Medicine of a General Hospital in Rome during a three year time period (1st January 2007–31 December 2009). Age, sex, period of hospitalization, clinical diagnosis, autoptic diagnosis, cause of death, and level of discrepancy between clinical diagnosis and autoptic control have been collected. Main diagnoses have been classified as follows: (1) discrepancy with a possible influence on survival; (2) discrepancy with no or questionable influence on survival. Ninety-two cases (42 males, 50 females; mean age 79.3 years) have been included. Thirty-four main diagnoses (36.9%) have been classified as discrepant (15.2% classified as type 1 discrepancy and 21.7 as type 2). A substantial discrepancy between clinical diagnosis and autoptic control is confirmed by the current study, which supports the role of autopsy as a tool for the improvement of medical practice.

Potential clinical impact of three-dimensional visualization for fluorescent in situ hybridization image analysis

Chromosomal translocation is strong indication of cancers. Fluorescent in situ hybridization (FISH) can effectively detect this translocation and achieve high accuracy in disease diagnosis and prognosis assessment. For this purpose, whole chromosome paint probes are utilized to image the configuration of DNA fragments. Although two-dimensional (2-D) microscopic images are typically used in FISH signal analysis, we present a case where the translocation occurs in the depth direction where two probed FISH signals are overlapped in the projected image plane. Thus, the translocation cannot be identified. However, when imaging the whole specimen with a confocal microscope at 27 focal planes with 0.5-μm step interval, the translocation can be clearly identified due to the free rotation capability by the three-dimensional (3-D) visualization. Such a translocation detection error of using 2-D images might be critical in detecting and diagnosing early or subtle disease cases where detecting a small number of abnormal cells can make diagnostic difference. Hence, the underlying implication of this report suggests that utilizing 3-D visualization may improve the overall accuracy of FISH analysis for some clinical cases. However, the clinical efficiency and cost of using 3-D versus 2-D imaging methods are also to be assessed carefully.

Combining multiple continuous tests for the diagnosis of kidney impairment in the absence of a gold standard

Receiver operating characteristic (ROC) curves are commonly used to summarize the classification accuracy of diagnostic tests. It is not uncommon in medical practice that multiple diagnostic tests are routinely performed or multiple disease markers are available for the same individuals. When the true disease status is verified by a gold standard (GS) test, a variety of methods have been proposed to combine such potential correlated tests to increase the accuracy of disease diagnosis. In this article, we propose a method of combining multiple diagnostic tests in the absence of a GS. We assume that the test values and their classification accuracies are dependent on covariates. Simulation studies are performed to examine the performance of the combination method. The proposed method is applied to data from a population-based aging study to compare the accuracy of three screening tests for kidney function and to estimate the prevalence of moderate kidney impairment.

Advanced proteomics procedure as a detection tool for predictive screening in type 2 pre-Diabetes

It has been suggested that a more precise selection of predictive biomarkers may prove useful in the early diagnosis of type 2 diabetes (T2D), even when glucose tolerance is normal. This is vital since many T2D cases may be preventable by avoiding those factors that trigger the disease process (primary prevention) or by use of therapy that modulates the disease process before the onset of clinical symptoms (secondary prevention) occurs. The selection of predictive markers must be carefully assessed and depends mainly on three important parameters: sensitivity, specificity and positive predictive value. Unfortunately, biomarkers with ideal specificity and sensitivity are difficult to find. One potential solution is to use the combinatorial power of different biomarkers, each of which alone may not offer satisfactory specificity and sensitivity. Recent technological advances in proteomics and bioinformatics offer a great opportunity for the discovery of different potential predictive markers. In this review, we described a cellular T2D model as an example with the intent of providing specific enrichment and new identification strategies, which might have the potential to improve predictive biomarker identification and to bring accuracy in disease diagnosis and classification, as well as therapeutic monitoring in the early phase of T2D.

Metabolite Quantification in Tumours by Magnetic Resonance Spectroscopy: Objectives, Results and Perspectives

Magnetic resonance spectroscopy (MRS) allows non invasive detection of tissue metabolism. Absolute or relative quantification of metabolites and chemical compounds detected by 1H and 31P MRS can today be performed in a number of pathological tissues including different types of cancer lesions, consequently improving accuracy in disease diagnosis and prognosis. Methods allowing quantification of tumour metabolites under in vivo MR spectral profiles have been progressively developed in the past and are now entering the clinical routine. Several methods of quantification have been proposed and validated in healthy tissues in a number of single- and multi-centre studies. Most of these approaches are not always directly applicable to cancer lesions because of their inherent heterogeneity or variability in water content, thus peak area ratios can, in some cases, represent better indicators of disease progression and response to therapy. An additional important limitation may derive from the excessive duration of some MRS measurement protocols, which would be added to a routine clinical MRI examination in the same session. We propose a critical overview of the principal methods currently applied for the quantification of metabolites and chemical compounds, mostly detected by 1H MRS in tumours.

Location ofXanthomonas translucensin pistachio trees

Xanthomonas translucens has been identified as the causal agent of pistachio dieback in Australia. Symptoms include decline, xylem staining, trunk and limb lesions, and excessive exudation of resin. Bacteria were previously isolated from stained wood in 2-year-old twigs but little was known about their presence in other parts of the tree. The pattern of staining and location of X. translucens were studied following felling and dissection of asymptomatic and diseased trees. Chestnut-coloured smears and specks occurred in the sapwood of diseased trees and were continuous from the trunk to 1–2-year-old twigs. X. translucens was isolated mainly from young sapwood (stained and unstained) of the main trunk, primary and younger branches and current season growth, less frequently from leaves and bunches, rarely from old, stained heartwood and not from roots and associated soil samples. Bacteria and pathogenic fungi were not found in the inner bark and cortex associated with lesions whereas the stained sapwood underlying the lesions yielded X. translucens. Scanning electron microscopy revealed bacteria in the main vessels of the xylem of stained tissue and tyloses in the proximity of colonised tissue. Information on the pattern of staining and location of the bacteria will facilitate pathogen detection, thereby improving the accuracy of disease diagnosis.

The application of genomic and proteomic technologies in predictive, preventive and personalized medicine

The long asymptomatic period before the onset of chronic diseases offers good opportunities for disease prevention. Indeed, many chronic diseases may be preventable by avoiding those factors that trigger the disease process (primary prevention) or by use of therapy that modulates the disease process before the onset of clinical symptoms (secondary prevention). Accurate prediction is vital for disease prevention so that therapy can be given to those individuals who are most likely to develop the disease. The utility of predictive markers is dependent on three parameters, which must be carefully assessed: sensitivity, specificity and positive predictive value. Specificity is important if a biomarker is to be used to identify individuals either for counseling or for preventive therapy. However, a reciprocal relationship exists between sensitivity and specificity. Thus, successful biomarkers will be highly specific without sacrificing sensitivity. Unfortunately, biomarkers with ideal specificity and sensitivity are difficult to find for many diseases. One potential solution is to use the combinatorial power of a large number of biomarkers, each of which alone may not offer satisfactory specificity and sensitivity. Recent technological advances in genetics, genomics, proteomics, and bioinformatics offer a great opportunity for biomarker discovery. The newly identified biomarkers have the potential to bring increased accuracy in disease diagnosis and classification, as well as therapeutic monitoring. In this review, we will use type 1 diabetes (T1D) as an example, when appropriate, to discuss pertinent issues related to high throughput biomarker discovery.