1,115 research outputs found

    In Silico Syndrome Prediction for Coronary Artery Disease in Traditional Chinese Medicine

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    Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM

    Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

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    IntroductionDementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).MethodsAtlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).ResultsThe binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.DiscussionResults suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease

    Combined Evaluation of FDG-PET and MRI Improves Detection and Differentiation of Dementia

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    INTRODUCTION: Various biomarkers have been reported in recent literature regarding imaging abnormalities in different types of dementia. These biomarkers have helped to significantly improve early detection and also differentiation of various dementia syndromes. In this study, we systematically applied whole-brain and region-of-interest (ROI) based support vector machine classification separately and on combined information from different imaging modalities to improve the detection and differentiation of different types of dementia. METHODS: Patients with clinically diagnosed Alzheimer's disease (AD: n = 21), with frontotemporal lobar degeneration (FTLD: n = 14) and control subjects (n = 13) underwent both [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) scanning and magnetic resonance imaging (MRI), together with clinical and behavioral assessment. FDG-PET and MRI data were commonly processed to get a precise overlap of all regions in both modalities. Support vector machine classification was applied with varying parameters separately for both modalities and to combined information obtained from MR and FDG-PET images. ROIs were extracted from comprehensive systematic and quantitative meta-analyses investigating both disorders. RESULTS: Using single-modality whole-brain and ROI information FDG-PET provided highest accuracy rates for both, detection and differentiation of AD and FTLD compared to structural information from MRI. The ROI-based multimodal classification, combining FDG-PET and MRI information, was highly superior to the unimodal approach and to the whole-brain pattern classification. With this method, accuracy rate of up to 92% for the differentiation of the three groups and an accuracy of 94% for the differentiation of AD and FTLD patients was obtained. CONCLUSION: Accuracy rate obtained using combined information from both imaging modalities is the highest reported up to now for differentiation of both types of dementia. Our results indicate a substantial gain in accuracy using combined FDG-PET and MRI information and suggest the incorporation of such approaches to clinical diagnosis and to differential diagnostic procedures of neurodegenerative disorders

    Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

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    Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes

    Characteristic Analysis from Excessive to Deficient Syndromes in Hepatocarcinoma Underlying miRNA Array Data

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    Traditional Chinese medicine (TCM) treatment is regarded as a safe and effective method for many diseases. In this study, the characteristics among excessive, excessive-deficient, and deficient syndromes of Hepatocellular carcinoma (HCC) were studied using miRNA array data. We first calculated the differentially expressed miRNAs based on random module t-test and classified three TCM syndromes of HCC using SVM method. Then, the weighted miRNA-target networks were constructed for different TCM syndromes using predicted miRNA targets. Subsequently, the prioritized target genes of upexpression network of TCM syndromes were analyzed using DAVID online analysis. The results showed that there are distinctly different hierarchical cluster and network structure of TCM syndromes in HCC, but the excessive-deficient combination syndrome is extrinsically close to deficient syndrome. GO and pathway analysis revealed that the molecular mechanisms of excessive-deficient and deficient syndromes of HCC are more complex than excessive syndrome. Furthermore, although excessive-deficient and deficient syndromes have similar complex mechanisms, excessive-deficient syndrome is more involved than deficient syndrome in development of cancer process. This study suggested that miRNAs might be important mediators involved in the changing process from excessive to deficient syndromes and could be potential molecular markers for the diagnosis of TCM syndromes in HCC

    Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

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    Background: Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective: The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods: Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results: We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions: DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools

    Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks

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    Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their accuracy is still limited. In this work we demonstrate a full automatic computer system to assist the diagnosis of PD using 18F-DMFP PET data. First, a few regions of interest are selected by means of a two-sample t-test. The accuracy of the selected regions to separate PD from APS patients is then computed using a support vector machine classifier. The accuracy values are finally used to train a Bayesian network that can be used to predict the class of new unseen data. This methodology was evaluated using a database with 87 neuroimages, achieving accuracy rates over 78%. A fair comparison with other similar approaches is also provided.This work is part of a project approved by the Andalucía Talent Hub Program launched by the Andalusian Knowledge Agency, co-funded by the European Union's Seventh Framework Program, Marie Sklodowska-Curie actions (COFUND Grant Agreement no 291780) and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía. The work was also supported by the University of Granada (Spain), the University for Munich (Germany), the MICINN (Spain) under the TEC2012–34306 project and the Consejera de Innovacin, Ciencia y Empresa (Junta de Andaluca, Spain) under the P11–TIC–7103 excellence project
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