Neuroscience informatics最新文献

The impact of metacognition on pupils' moral ideals and emotional development was investigated as well as it highlights on a collaborative research between metacognition and artificial intelligence that can bridge the gap (emotional, ethical, moral reasoning, common sense) existing in AI. A total of 200 pupils were selected in the study's sample. Participants (100 high metacognitive students and 100 low metacognitive students) were chosen at random and ranged in age from 17 to 21 years old. The influence of metacognition on students' moral ideals and emotional development was studied using a t-test. The outcome reveals that the mean score of moral reasoning on high metacognitive students as 66.77 and for low metacognitive students as 63.08, t value = 3.21, at the 0.01 level, statistically highly significant. The mean emotional maturity score for high metacognitive students was 29.99, while for low metacognitive students was 33.01, t value as 2.81, shows statistically significant at the 0.05 level. This demonstrates that the higher the score, the less emotionally stable the pupils are. The current findings show that metacognitive thinking has a major impact on moral reasoning and emotional maturity, and that as metacognition levels rise, so do moral reasoning and emotional maturity. Metacognition can strengthen the humanistic qualities which are majorly lacking in AI. In addition, there are new avenues being opened in the study of artificial intelligence via metacognitive study which is significant and futuristic.

Background: Individuals with visual impairment currently rely on walking sticks and guide dogs for mobility. However, both tools require the user to have a mental map of the area and cannot help the user establish detailed information about their surroundings, including weather, location, and businesses.

Purpose and Methods: This study designed a navigation and recommendation system with context awareness for individuals with visual impairment. The study used Process for Agent Societies Specification and Implementation (PASSI), which is a multiagent development methodology that follows the Foundation for Intelligent Physical Agents framework. The model used the Agent Unified Modeling Language (AUML).

Results: The developed system contains a context awareness module and a multiagent system. The context awareness module collects data on user context through sensors and constructs a user profile. The user profile is transferred to the multiagent system for service recommendations. The multiagent system has four agents: a consultant agent, search agent, combination agent, and dispatch agent and integrates machine and deep learning. AUML tools were used to describe the implementation and structure of the system through use-case graphics and kit, sequence, class, and status diagrams.

Conclusions: The developed system understands the needs of the user through the context awareness module and finds services that best meet the user's needs through the agent recommendation mechanism. The system can be used on Android phones and tablets and improves the ease with which individuals with visual impairment can obtain the services they need.

Data mining techniques have taken a significant role in the diagnosis and prognosis of many health diseases. Still, very little work has been initialized in neurological medical informatics or neurodegenerative disease. Parkinson's Disease (PD) is the second significant neurodegenerative disease (after Alzheimer's), which causes severe complications for patients. PD is a nervous disorder that affects millions of people worldwide. Most of the cases go undetected due to a lack of standard detection methods. This paper attempts to review literature related to PD diagnosis, its stages, and its management using data mining techniques (DMT). The review has been done by exploring the Scopus indexed literature using the query containing the keywords data-mining and Parkinson's disease. This study's focus is to observe how DMT, its applications have developed in PD during the past 16 years. This paper reviews data mining techniques, their applications, and development, through a review of the literature and articles' classification, from 2004 to 2020. We have used keyword indices and article abstracts to identify 273 articles concerning DMT applications from 159 academic journals from Scopus online database. Another objective of this paper is to provide directions to researchers in data mining applications in Parkinson's disease.

Motivation: Healthcare systems globally face significant resource and financial challenges. Moreover, these challenges have resulted in an existential paradigm shift driven by: (i) the growth in the demand for healthcare services is exacerbated by a global population characterised by an ageing demographic with increasingly complex healthcare needs, and (ii) rapid developments in healthcare technologies and drug therapies which can be seen in the new and emerging treatment options. A potential solution to address [or at least mitigate] these challenges is ‘telemedicine’ with nurse-led ‘triage’ systems; however, a limiting factor for ‘telemedicine’ is the management of imprecision and uncertainty in the diagnostic process. Contribution: In this paper we introduce a novel rule-based approach predicated on picture fuzzy sets to enable intelligent clinical decision support system which builds on previous research to create an approach predicated on picture fuzzy sets. Our principal contribution lies in the use of expert clinician preferences in a rule-based system which implements knowledge reasoning along with linguistic information to improve the diagnostic performance. Results: In ‘real-world’ case studies (using ethically approved anonymised patient data) we have investigated heart conditions, kidney stones, and kidney infections. Reported results for the proposed approach demonstrate a high level of accuracy in clinical diagnostic accuracy terms with reported accuracy in the range [92% to 95%] and a high confidence level when compared to alternative diagnostic matching methods.

This paper presents a method for the automatic labeling of vascular bifurcations along the Circle of Willis (CoW) in 3D images. Our automatic labeling process uses machine learning as well as dimensionality reduction algorithms to map selected bifurcation features to a lower dimensional space and thereafter classify them. Unlike similar studies in the literature, our main goal here is to avoid a classical registration step commonly applied before resorting to classification. In our approach, we aim to collect various geometric features of the bifurcations of interest, and thanks to dimensionality reduction, to discard the irrelevant ones before using classifiers.

In this paper, we apply the proposed method to 50 human brain vascular trees imaged via Magnetic Resonance Angiography (MRA). The constructed classifiers were evaluated using the Leave One Out Cross-Validation approach (LOOCV). The experimental results showed that the proposed method could assign correct labels to bifurcations at 96.8% with the Naive Bayes classifier. We also confirmed its functionality by presenting automatic bifurcation labels on independent images.

Retinal vascular changes are the early indicators for many progressive diseases like diabetes, hypertension, etc. However, the manual procedure in detecting these vascular changes is a time-consuming process and may cause a large variance, especially when dealing with a large dataset. Therefore, computer-aided diagnosis of the retinal vascular network plays a crucial role in analyzing the patients effectively with high precision. As a result, this paper presents a robust deep learning Multistage Dual-Path Interactive Refinement Network (DPIRef-Net) for segmenting the vascular maps of arteries and veins from the retinal surface. The main novelty of the proposed model lies in segmenting both the regional and edge salient feature maps that will reduce the degeneration problems of pooling and striding. This eventually preserves the edges of vascular branches and suppresses the false positive rate. In addition to this, a novel guided filtering technique is employed to segment the final accurate arteries and veins vascular networks from predicted regional and edge feature maps. The proposed Multistage DPIRef-Net is trained and tested on different benchmark datasets like DRIVE, HRF, AVRDB, INSPIRE AVR, VICAVR, and Dual-Mode datasets. The proposed model illustrated superior performance in segmenting the vascular maps on all datasets by achieving an average accuracy of 97%, a sensitivity of 96%, a specificity of 98%, and a dice coefficient of 98%.

Objective

There have been few reports of the outcomes of flow re-direction endoluminal device (FRED) treatment for unruptured cerebral aneurysms, and patient factors associated with effective aneurysm obliteration have yet to be determined. Flow diverters also have problems with delayed rupture. The objective of this study was to investigate associations between the cases of early obliteration of aneurysm after FRED treatment and a range of factors.

Method

A retrospective analysis of 75 aneurysms in 72 patients whose response to treatment was evaluated by cerebral angiography 6 months after FRED treatment was conducted. The aneurysm obliteration rate was classified according to the O'Kelly-Marotta grading scale (OKM grade). The patients were classified into those assessed as OKM Grade A or Grade B with poor aneurysm obliteration (poor obliteration), and those assessed as Grade C or Grade D with good aneurysm obliteration (good obliteration). The parameters evaluated were age, sex, medical history, immediate postoperative eclipse sign, P2Y12 reaction units (PRU), aspirin reaction units (ARU), operating time, maximum aneurysm diameter measured on cerebral angiography, and aneurysm location.

Results

At 6 months post-treatment, 19 aneurysms (25.3%) were OKM Grade A, 15 (20%) were Grade B, 10 (13.3%) were Grade C, and 31 (41.3%) were Grade D. Age ≥67.5 years was significantly associated with a poor obliteration [odds ratio (OR): 0.1; 95% confidence interval (95%CI): 0.2-0.4; $p=0.002$] and intracranial side wall aneurysm [OR: 21.7; 95%CI: 1.6–284.5; $p=0.01$].

Conclusions

The results of this study demonstrated that age was associated with aneurysm obliteration after FRED treatment. This finding may be useful for further studies investigating factors predictive of the aneurysm obliteration rate and the residual aneurysm rate after FRED treatment.

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits.

A correct diagnosis of brain tumours is crucial to making an accurate treatment plan for patients with the disease and allowing them to live a long and healthy life. Among a few clinical imaging modalities, attractive reverberation imaging gives extra different data about the tissues. The use of MRI-Magnetic Resonance Imaging tests is a significant method for identifying disorders throughout the human body. Deep learning provides a solution for efficiently detecting Brain Tumour. The work has used MRI images for predicting the glioblastoma of brain tumours. Initially, data is retrieved from hospitals in form of an image database to continue with the brain tumour prediction. Pre-processing of dataset images is a mandatory step to enhance the accuracy and smooth line supplementary stages. The intensity value of each MRI (Magnetic Resonance Imaging) is subtracted by the mean intensity value and standard deviation of the brain region. Further, reduce the medical image noise by employing a bilateral filter. Further, the preprocessed medical images are used for extracting the radiomics features from images as well as tumour segmentation. Thus the work adopts the tumor is automatically segmented into four compartments using mutually exclusive rules using Modified Fuzzy C Means Clustering (MFCM). The clustering-based approach is very beneficial in MR tumour segmentation; it categorizes the pixels using certain radiomics features. The most important problem in the radiomics-based machine learning model is the dimension of data. Moreover, using a GWO (Grey Wolf Optimizer) with rough set theory, we propose a novel dimensionality reduction algorithm. This method is employed to find the significant features from the extracted images and differentiate HG (high-grade) and LG (Low-grade) from GBM while varying feature correlation limits were applied to remove redundant features. Finally, the article proposed the dynamic architecture of Multilevel Layer modelling in Faster R-CNN (MLL-CNN) approach based on feature weight factor and relative description model to build the selected features. This reduces the overall computation and performs long-tailed classification. This results in the development of CNN training performance more accurate. Results show that the general endurance expectation of GBM cerebrum growth with more prominent exactness of about 95% with the decreased blunder rate to be 2.3%. In the calculation of similarity between segmented tissues and ground truth, different tools produce correspondingly different predictions.

Seizures, which last for a while and are a symptom of epilepsy, are bouts of excessive and abnormally synchronized neuronal activity in the patient's brain. For young children, in particular, early diagnosis and treatment are essential to optimize the likelihood of the best possible child-specific result. Electroencephalogram (EEG) signals can be inspected to look for epileptic seizures. However, certain epileptic patients with severe cases show high rates of misdiagnosis or failure to notice the seizures, and they do not demonstrate any improvement in healing as a result of their inability to respond to medical treatment. The purpose of this study was to identify EEG biomarkers that may be used to distinguish between children with epilepsy and otherwise healthy and normal subjects. Savitzky-Golay (SG) filter was used to record and analyze the data from 19 EEG channels. EEG background activity was used to calculate amplitude-aware permutation entropy (AAPE) and enhanced permutation entropy (impe). The hypothesis that the irregularity and complexity in epileptic EEG were decreased in comparison with healthy control participants was tested statistically using the t-test (p < 0,05). As a method of dimensionality reduction, principle component analysis (PCA) was used. The EEG signals of the patients with epileptic seizures were then separated from those of the control individuals using decision tree (DT) and random forest (RF) classifiers. The findings indicate that the EEG of the AAPE and impe was decreased for epileptic patients. A comparison study has been done to see how well the DT and RF classifiers work with the SG filter, AAPE and impe features, and PCA dimensionality reduction technique. When identifying patients with epilepsy and control subjects, PCA with DT and RF produced accuracies of 85% and 80%, respectively, but without the PCA, DT and RF showed accuracies of 75% and 72.5%, respectively. As a result, the EEG may be a trustworthy index for looking at short-term indicators that are sensitive to epileptic identification and classification.