Accuracy Of Disease Diagnosis Research Articles

How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.

SummaryThe clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid‐19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI‐based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid‐19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI‐based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid‐19 cases. AI‐based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid‐19 pandemic.

Hematological And Biochemical Blood Changes In Chronic Tendinitis Thoroughbred Race Horses

Tendon injury is the most important veterinary reason for wastage of thoroughbred racehorses. Clinical diagnosis of tendon injuries is confirmed by a combination of clinical, ultrasonographic or post mortem examination of the injured limb. In addition, measurement of hematological and biochemical blood parameters are an important tool that aid health assessment and decision-making in diagnosis, treatment and follow-up of the injured tendon. Therefore, the lack of information or misinterpretation of these parameters may affect the accuracy of disease diagnosis and then lead to poor treatment. The present study was conducted to evaluate the levels of some hematological and biochemical parameter in the blood of thoroughbred horses affected by chronic tendinitis and compared with normal horses. Blood samples were collected from 15 healthy thoroughbred horses (8 stallions and 7 mares) and 21 tendinitis thoroughbred horses (11 stallions and 10 mares); and the levels of 18 blood parameters were determined. The tendinitis horses had higher number of erythrocytes and thrombocytes, higher values of packed cell volume (PCV) and mean corpuscular volume (MCV); lower enzyme activity of creatine kinase (CK), lower values of lactic acid (LA), icteric index and mean corpuscular hemoglobin concentration (MCHC) and lower numbers of band neutrophils than the normal horses. The chronic tendinitis mares had higher number of thrombocytes and lower values of lactate dehydrogenase (LDH) enzyme activity, lactic acid, plasma proteins, MCHC and lower numbers of white blood cells (WBC) than the normal mares. The chronic tendinitis stallions had higher levels of lactic acid, plasma proteins, MCV, and higher numbers of erythrocytes and thrombocytes,; and lower values of icteric jaundice, MCHC, band neutrophils than the normal horses. No significant differences were reported when tendinitis mares were compared with tendinitis males. However, normal mares showed higher levels of plasma proteins than normal stallions. The results obtained by this study can be used as useful index to diagnose and treat chronic tendinitis in horses.

Fluorescence imaging and Raman spectroscopy applied for the accurate diagnosis of breast cancer with deep learning algorithms.

Deep learning is usually combined with a single detection technique in the field of disease diagnosis. This study focused on simultaneously combining deep learning with multiple detection technologies, fluorescence imaging and Raman spectroscopy, for breast cancer diagnosis. A number of fluorescence images and Raman spectra were collected from breast tissue sections of 14 patients. Pseudo-color enhancement algorithm and a convolutional neural network were applied to the fluorescence image processing, so that the discriminant accuracy of test sets, 88.61%, was obtained. Two different BP-neural networks were applied to the Raman spectra that mainly comprised collagen and lipid, so that the discriminant accuracy of 95.33% and 98.67% of test sets were gotten, respectively. Then the discriminant results of fluorescence images and Raman spectra were counted and arranged into a characteristic variable matrix to predict the breast tissue samples with partial least squares (PLS) algorithm. As a result, the predictions of all samples are correct, with minor error of predictive value. This study proves that deep learning algorithms can be applied into multiple diagnostic optics/spectroscopy techniques simultaneously to improve the accuracy in disease diagnosis.

A Novel Classification Method Based on Multilayer Perceptron-Artificial Neural Network Technique for Diagnosis of Chronic Kidney Disease

Background: Chronic kidney disease is one of the most common diseases. The early diagnosis of this disease will reduce the length of treatment and decrease high medical costs. In recent years, the use of computer techniques in data mining and intelligent algorithms has accelerated the early diagnosis of this disease. One of the intelligent methods to diagnose this disease is artificial intelligence networks. Objectives: This study aimed to investigate the diagnosis of chronic kidney disease using an artificial intelligence network based on the multilayer perceptron method. Methods: The data of laboratory samples were collected from 140 healthy people and patients with chronic kidney disease. After preprocessing and normalization, the data were given to a multilayer perceptron and the accuracy of disease diagnosis was evaluated. All analyses were performed using MATLAB software. Results: The simulation showed a 98% accuracy of diagnosis using the proposed model. Conclusions: The results of real data suggested that the proposed system was more effective and faster than other methods in the diagnosis of acute kidney disease and it can be used as a physician assistant tool in clinical practice. In addition, it can be a cost-effective method for patients.

Kernelized Normal Discriminant Feature Selection and Borda Count Bootstrap Aggregating Classification for Risk Factor Identification and Disease Diagnosis

Automatic detection of disease is crucial in health care management to evaluate large patient data. The early diagnosis and treatment of disease are an important task to prevent the patient from death. The various researchers have contributed to the development of disease diagnosis. But still, it causes the more risk for identifying the patient health conditions. In order to improve the disease diagnosing accuracy, A Kernelized Normal discriminant Feature Selection based Borda count bootstrap aggregating Classification (KNDFS-BCBAC) technique is introduced for identifying the patient health condition and critical factor analysis with higher accuracy and lesser time. At first, radial basis kernelized normal discriminant analysis is used to identify the relevant feature for minimizing the complexity of disease diagnosis. After selecting the relevant features, Borda count bootstrap aggregating Classifier is applied to classify the patient data as abnormal or normal by constructing the weak learner as bivariate correlated regression tree. Then, the diseased data is considered as a training sample for analyzing the critical factor and classifies the patient data level as the initial stage, critical stage based on the threshold range of features value. By applying the Borda count voting scheme, the weak learner results are combined into strong. In this way, disease diagnosis and critical factor analysis of patient data are performed with greater accuracy and minimal time complexity (TC). Experimental is performed with tumor dataset on metrics namely disease diagnosis accuracy (DDA), false alarm rate (FAR), and TC. The observed results evident that KNDFSBCBAC technique achieves higher DDA with lesser complexity and FAR than the conventional methods.

Improvement of alzheimer disease diagnosis accuracy using ensemble methods

Nowadays, there is a significant increase in the medical data that we should take advantage of that. The application of the machine learning via the data mining processes, such as data classification depends on using a single classification algorithm or those complained as ensemble models. The objective of this work is to improve the classification accuracy of previous results for Alzheimer disease diagnosing. The Decision Tree algorithm with three types of ensemble methods combined, which are Boosting, Bagging and Stacking. The clinical dataset from the Open Access Series of Imaging Studies (OASIS) was used in the experiments. The experimental results of the proposed approach were better than the previous work results. Where the Random Forest (Bagging) achieved the highest accuracy among all algorithms with 90.69%, while the lowest one was Stacking with 79.07%. All these results generated in this paper are higher in accuracy than that done before.

Improvement of alzheimer disease diagnosis accuracy using ensemble methods

Nowadays, there is a significant increase in the medical data that we should take advantage of that. The application of the machine learning via the data mining processes, such as data classification depends on using a single classification algorithm or those complained as ensemble models. The objective of this work is to improve the classification accuracy of previous results for Alzheimer disease diagnosing. The Decision Tree algorithm with three types of ensemble methods combined, which are Boosting, Bagging and Stacking. The clinical dataset from the Open Access Series of Imaging Studies (OASIS) was used in the experiments. The experimental results of the proposed approach were better than the previous work results. Where the Random Forest (Bagging) achieved the highest accuracy among all algorithms with 90.69%, while the lowest one was Stacking with 79.07%. All these results generated in this paper are higher in accuracy than that done before.

Probit Regressed Feature Selection Based Linear Programming Boost Classification for Tumor Risk Factor Identification and Disease Diagnosis

Accurate diagnosis of survival rate in patients with tumor remains challenges due to the increasing complexity of treatment protocols, and different patient population samples. Due to the complexity, the risk factor of the patients gets increased. Therefore, a reliable and well-validated prediction needs to develop the automatic disease diagnosis for early detection of the tumor. The novel technique called Probit Regressed Feature Selection based Iterative Linear Programming Boost Classification (PRFS-ILPBC) is introduced for tumor risk factor identification and disease diagnosis of patient data with higher accuracy and lesser time consumption. In PRFS-ILPBC technique, Probit Regression model is a regression type to estimate the relationship between the features and the disease symptoms using bivariate correlation coefficient. Based on the correlation results, the features fall into any one of the two classes (i.e. relevant or irrelevant). With the help of relevant feature, Iterative Linear Programming (LP) Boost Classification model is applied to perform classification by combining the weak learner for tumor risk factor identification and disease diagnosis. LPBoost constructs the strong classifier through initiating with a set of weak classifiers. The training data (i.e. patient data) are taken as the input and added to the set of considered weak classifiers. The kernelized support vector machine act as weak learner compares the training features with the testing results to identify the risk factor and classify the patient data into normal or abnormal. The ensemble classifier improves disease diagnosis accuracy and reduces the false positive rate. Experimental evaluation of proposed PRFS-ILPBC technique and existing methods are carried out with different factors such as disease diagnosing accuracy, false positive rate, and computation time with respect to a number of patient data. The observed results reported that the proposed PRFS-ILPBC technique achieves higher disease diagnosing accuracy with minimum computation time as well as false positive rate than the conventional techniques.

Three-dimensional convolutional neural networks for simultaneous dual-tracer PET imaging

Dual-tracer positron emission tomography (PET) is a promising technique to measure the distribution of two tracers in the body by a single scan, which can improve the clinical accuracy of disease diagnosis and can also serve as a research tool for scientists. Most current research on dual-tracer PET reconstruction is based on mixed images pre-reconstructed by algorithms, which restricts the further improvement of the precision of reconstruction. In this study, we present a hybrid loss-guided deep learning based framework for dual-tracer PET imaging using sinogram data, which can achieve reconstruction by naturally unifying two functions: the reconstruction of the mixed images and the separation for individual tracers. Combined with volumetric dual-tracer images, we adopted a three-dimensional (3D) convolutional neural network (CNN) to learn full features, including spatial information and temporal information simultaneously. In addition, an auxiliary loss layer was introduced to guide the reconstruction of the dual tracers. We used Monte Carlo simulations with data augmentation to generate sufficient datasets for training and testing. The results were analyzed by the bias and variance both spatially (different regions of interest) and temporally (different frames). The analysis verified the feasibility of the 3D CNN framework for dual-tracer reconstruction. Furthermore, we compared the reconstruction results with a deep belief network (DBN), which is another deep learning based technique for the separation of dual-tracer images based on time–activity curves (TACs). The comparison results provide insights into the superior features and performance of the 3D CNN. Furthermore, we tested the [11C]FMZ–[11C]DTBZ images with three total-counts levels (, , ), which indicate different noise ratios. The analysis results demonstrate that our method can successfully recover the respective distribution of lower total counts with nearly the same accuracy as that of the higher total counts in the total counts range we applied, which also also indicates the proposed 3D CNN framework is more robust to noise compared with DBN.

Combining a gravitational search algorithm, particle swarm optimization, and fuzzy rules to improve the classification performance of a feed-forward neural network

Background and objectiveA feed-forward neural network (FNN) is a type of artificial neural network that has been widely used in medical diagnosis, data mining, stock market analysis, and other fields. Many studies have used FNN to develop medical decision-making systems to assist doctors in clinical diagnosis. The aim of the learning process in FNN is to find the best combination of connection weights and biases to achieve the minimum error. However, in many cases, FNNs converge to the local optimum but not the global optimum. Using open disease datasets, the purpose of this study was to optimize the connection weights and biases of the FNN to minimize the error and improve the accuracy of disease diagnosis. MethodIn this study, the chronic kidney disease (CKD) and mesothelioma (MES) disease datasets from the University of California Irvine (UCI) machine learning repository were used as research objects. This study applied the FNN to learn the features of each datum and used particle swarm optimization (PSO) and a gravitational search algorithm (GSA) to optimize the weights and biases of the FNN classifiers based on the algorithms inspired by the observation of natural phenomena. Moreover, fuzzy rules were used to optimize the parameters of the GSA to improve the performance of the algorithm in the classifier. ResultsWhen applied to the CKD dataset, the accuracies of PSO and GSA were 99%. By using fuzzy rules to optimize the GSA parameter, the accuracy of fuzzy–GSA was 99.25%. The accuracies of the combined algorithms PSO–GSA and fuzzy–PSO–GSA reached 100%. In the MES disease dataset, all methods exhibited good performance with 100% accuracy. ConclusionsThis study used PSO, GSA, fuzzy–GSA, PSO–GSA, and fuzzy–PSO–GSA on CKD and MES disease datasets to identify the disease, and the performance of different algorithms was explored. Compared with other methods in the literature, our proposed method achieved higher accuracy

Boosted neural network ensemble classification for lung cancer disease diagnosis

Accurate diagnosis of Lung Cancer Disease (LCD) is an essential process to provide timely treatment to the lung cancer patients. Artificial Neural Networks (ANN) is a recently proposed Machine Learning (ML) algorithm which is used on both large-scale and small-size datasets. In this paper, an ensemble of Weight Optimized Neural Network with Maximum Likelihood Boosting (WONN-MLB) for LCD in big data is analyzed. The proposed method is split into two stages, feature selection and ensemble classification. In the first stage, the essential attributes are selected with an integrated Newton–Raphsons Maximum Likelihood and Minimum Redundancy (MLMR) preprocessing model for minimizing the classification time. In the second stage, Boosted Weighted Optimized Neural Network Ensemble Classification algorithm is applied to classify the patient with selected attributes which improves the cancer disease diagnosis accuracy and also minimize the false positive rate. Experimental results demonstrate that the proposed approach achieves better false positive rate, accuracy of prediction, and reduced delay in comparison to the conventional techniques.

Enhancing Photoacoustic Intensity of Upconversion Nanoparticles by Photoswitchable Azobenzene‐Containing Polymers for Dual NIR‐II and Photoacoustic Imaging In Vivo

AbstractNear‐infrared photoacoustic (PA) imaging is developing rapidly as an alternative to optical imaging and promises benefits in terms of deeper tissue penetration and higher spatial resolution. This paper proposes a strategy to enhance the PA intensity of upconverted nanoparticles (UCNPs) by functionalized photoswitchable azobenzene‐containing poly (acrylic acid) (UCNPs@PAA‐Azo) to yield a PA signal that is up to six times higher than that of UCNPs without a decay in NIR‐II emission. The mechanism of enhanced photoacoustic activity of the UCNPs@PAA‐Azo can be attributed to the decay of UV and blue light as well as vibrations of the azobenzene group. This technology delivers high resolution of the hind limb vasculature using dual NIR‐II and PA imaging. Using this nanoprobe, NIR‐II imaging provides real‐time means for the precise resection of the lymph node (LN) during surgery, where PA imaging allows for quicker (up to 30 min) detection of LNs than that through NIR‐II imaging. Combining the advantages of dual NIR‐II and PA modalities allows us to achieve complementary information and improve the accuracy of disease diagnosis. This work is critical to the synthesis of dual NIR‐II and PA modal probes and is utilized in small animals imaging with high resolution and a high signal‐to‐noise ratio.

Diagnosis and Data Probability Decision Based on Non-Small Cell Lung Cancer in Medical System

There are many factors affecting the survival of people in developing countries, such as the tremendous number of population, nonuniform medical resources, and the threatening of malignant diseases. The improvements in medical information system in developing countries may lead to a bright future. By using effect medical resources and utilizing the information coming from the medical system, the doctors could come to a diagnosis with analysis. The probability of getting sick is very useful information which assists doctors to improve the accuracy of disease diagnosis, shortening treatment time, and reducing the incidence of misdiagnosis. This paper aims to build a model, considering not only probability analysis but also decision making, which can play a crucial role to figure out the probability of non-small lung cancer transitions in four different stages. In each process of the model, selecting effective parameters with big data are adopted for finding maximum effect with the top three high relevancy diagnose and decision data. With effective treatment methods that improve the relevancy diagnose data, the probability of malignant disease development will decrease. It is proved by the statistical analysis of clinical data that the model provides clinical data fast with enough accuracy.

Element-specific spectral imaging of multiple contrast agents: a phantom study

This work demonstrates the feasibility of simultaneous discrimination of multiple contrast agents based on their element-specific and energy-dependent X-ray attenuation properties using a pre-clinical photon-counting spectral CT. We used a photon-counting based pre-clinical spectral CT scanner with four energy thresholds to measure the X-ray attenuation properties of various concentrations of iodine (9, 18 and 36 mg/ml), gadolinium (2, 4 and 8 mg/ml) and gold (2, 4 and 8 mg/ml) based contrast agents, calcium chloride (140 and 280 mg/ml) and water. We evaluated the spectral imaging performances of different energy threshold schemes between 25 to 82 keV at 118 kVp, based on K-factor and signal-to-noise ratio and ranked them. K-factor was defined as the X-ray attenuation in the K-edge containing energy range divided by the X-ray attenuation in the preceding energy range, expressed as a percentage. We evaluated the effectiveness of the optimised energy selection to discriminate all three contrast agents in a phantom of 33 mm diameter. A photon-counting spectral CT using four energy thresholds of 27, 33, 49 and 81 keV at 118 kVp simultaneously discriminated three contrast agents based on iodine, gadolinium and gold at various concentrations using their K-edge and energy-dependent X-ray attenuation features in a single scan. A ranking method to evaluate spectral imaging performance enabled energy thresholds to be optimised to discriminate iodine, gadolinium and gold contrast agents in a single spectral CT scan. Simultaneous discrimination of multiple contrast agents in a single scan is likely to open up new possibilities of improving the accuracy of disease diagnosis by simultaneously imaging multiple bio-markers each labelled with a nano-contrast agent.

Missing value imputation for breast cancer diagnosis data using tensor factorization improved by enhanced reduced adaptive particle swarm optimization

Cancer refers to a disease in which a group of cells show uncontrolled growth, invasion and metastasis. Data mining and machine learning are common approaches for clinical diagnosis. An important issue in this filed is that these data often include missing value which reduces the diagnostic accuracy. In this paper, a modified tensor factorization method is used for estimating the missing data. When tensor approach intends to estimate the missing value, there should be a class balance in the dataset and tensor does not properly estimate data in the case of data insufficiency. In the proposed schema, particle swarm optimization algorithm with adaptive adjustment (RAPSO) which is improved by chaotic search is used to solve this problem. In this approach, in order to inhibit the random initialization of the particle swarm algorithm, a distinctive approach is adopted and Bayesian network is used. Finally, the accuracy of disease diagnosis is estimated through different classifiers and RMSE measure. The results suggested that the proposed method is superior to other methods in terms of RMSE, accuracy, sensitivity and specificity.

Optimize Transfer Learning for Lung Diseases in Bronchoscopy Using a New Concept: Sequential Fine-Tuning.

Bronchoscopy inspection, as a follow-up procedure next to the radiological imaging, plays a key role in the diagnosis and treatment design for lung disease patients. When performing bronchoscopy, doctors have to make a decision immediately whether to perform a biopsy. Because biopsies may cause uncontrollable and life-threatening bleeding of the lung tissue, thus doctors need to be selective with biopsies. In this paper, to help doctors to be more selective on biopsies and provide a second opinion on diagnosis, we propose a computer-aided diagnosis (CAD) system for lung diseases, including cancers and tuberculosis (TB). Based on transfer learning (TL), we propose a novel TL method on the top of DenseNet: sequential fine-tuning (SFT). Compared with traditional fine-tuning (FT) methods, our method achieves the best performance. In a data set of recruited 81 normal cases, 76 TB cases and 277 lung cancer cases, SFT provided an overall accuracy of 82% while other traditional TL methods achieved an accuracy from 70% to 74%. The detection accuracy of SFT for cancers, TB, and normal cases are 87%, 54%, and 91%, respectively. This indicates that the CAD system has the potential to improve lung disease diagnosis accuracy in bronchoscopy and it may be used to be more selective with biopsies.

Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD.

Endobronchial ultrasound-guided transbronchial needle aspiration versus cervical mediastinoscopy in lung cancer diagnosis and mediastinal staging: A retrospective comparison study.

e20066 Background: Invasive mediastinal lymph node staging is essential for resectable lung cancers. This retrospective study compares the diagnosis yield of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and cervical mediastinoscopy (CMS). Methods: Consecutive patients were analyzed from Jan 2009 to March 2016. Only pathologically confirmed results were accepted, and systematic mediastinal lymphadenectomy (SML) were used as the standard. The disease diagnosis and N stagingaccuracywere compared in this study. Results: 103 EBUS-TBNA patients and 232 CMS patients were included, 1014 mediastinal lymph nodes were biopsied in lung cancer patients. In per case analysis, there was no significant differences between EBUS-TBNA and CMS in disease diagnosis accuracy (89.4% vs. 81.2%, P = 0.097), and no significant difference in N staging accuracy (75.0% vs. 78.3%, P = 0.629). However, EBUS-TBNA had significantly higher disease diagnosis sensitivity than CMS (82.4% vs. 47.6%, P < 0.001). In lymph nodes diagnosis comparison (station #2, #4 and #7), both EBUS-TBNA and CMS showed very high accuracy, sensitivity, and specificity (94.7% vs. 99.6%, 88.6% vs. 94.8%, 97.2% vs. 100%), however CMS were slightly better. Positive lymph nodes had longer major and minor axes than negative nodes, and the positive rateswere as high as 59.2% in lymph nodes with a minor axis measuring ≥21mm. More complications and injuries were found in patients receiving CMS. Conclusions: For clinically suspected lung cancers, both EBUS-TBNA and CMS are favorable options for invasive mediastinal staging. EBUS-TBNA may be preferred for its higher disease diagnosis sensitivity and fewer complications.

Improving the diagnosis of bovine tuberculosis in farmed deer

CONTROL of bovine tuberculosis (TB) caused by Mycobacterium bovis presents a considerable challenge as this bacterium infects a wide host range. M bovis infection in farmed and wild deer is...

Accurate and visual discrimination of single-base mismatch by utilization of binary DNA probes in gold nanoparticles-based biosensing strategy

Herein we report a colorimetric biosensing strategy to discriminate single-nucleotide mutation in DNA with high selectivity using unmodified gold nanoparticles (AuNPs) as indicators. In the AuNPs-based colorimetric strategy, binary DNA probes were produced by splitting a long DNA probe in the middle for sensitive differentiation of single-base mismatch. The detection limit of this method toward target DNA was 5nM. The developed system has superior advantages of utilization of inexpensive materials, simplicity and visualization. Moreover, binary DNA probes not only can distinguish single-base mutation in the target DNA very well, as compared to long DNA probe, but also can construct “AND” logic gate using two distinct target DNAs as inputs, which holds great potential for increasing the accuracy of disease diagnosis in clinical applications.