Neuroscience informatics最新文献

The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multiclass Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that use two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances are compared using different classification metrics.

Introduction

In modern days, checking the huge number of MRI (magnetic resonance imaging) images and finding a brain tumour manually by a human is a very tedious and inaccurate task. It can affect the proper medical treatment of the patient. Again, it can be a hugely time-consuming task as it involves a huge number of image datasets. There is a good similarity between normal tissue and brain tumour cells in appearance, so segmentation of tumour regions become a difficult task to do. So there is an essentiality for a highly accurate automatic tumour detection method.

Method

In this paper, we proposed an algorithm to segment brain tumours from 2D Magnetic Resonance brain Images (MRI) by a convolutional neural network which is followed by traditional classifiers and deep learning methods. We have taken various MRI images with diverse Tumour sizes, locations, shapes, and different image intensities to train the model well. Furthermore, we have applied SVM classifier and other activation algorithms (softmax, RMSProp, sigmoid, etc) to cross-check our work. We implement our proposed method using “TensorFlow” and “Keras” in “Python” as it is an efficient programming language to perform fast work.

Result

In our work, CNN gained an accuracy of 99.74%, which is better than the state of the result obtained so far.

Conclusion

Our CNN based model will help the doctors to detect brain tumours in MRI images accurately, so that the speed in treatment will increase a lot.

Purpose

Different imaging sequences (T1 etc.) depict different aspects of a brain tumor. As clinical MRI examinations of the brain might be terminated prematurely, not all sequences may be acquired, decreasing the performance of automated tumor segmentation. We attempt to optimize the order of sequences, to maximize information gain in case of incomplete examination.

Methods

For segmentation we used the winner algorithm of the Brain Tumor Segmentation challenge 2018, trained on the BraTS 2020 dataset, with the objective to segment necrotic core, peritumoral edema, and enhancing tumor. We compared the segmentation performance for all combinations of sequences, using the Dice score (DS) as the primary metric. We compare the results with those which would be obtained by attempting to follow the consensus recommendations for brain tumor imaging [T1, FLAIR, T2, T1CE].

Results

The average segmentation accuracy varies between 0.476 for T1 only and 0.751 for the full set of sequences. T1CE has a high information content, even regarding peritumoral edema and information of T2 and FLAIR were highly redundant. The optimal order of sequences appears to be [T1, T2, T1CE, FLAIR]. Comparing segmentation accuracy after each fully acquired sequence, the first sequence (T1) is the same for both, DS for [T1, T2] (proposed) is 6.2% higher than [T1, FLAIR] (aborted recommendations), and [T1, T2, T1CE] (proposed) is 34.8% higher than [T1, FLAIR, T2] (aborted recommendations).

Conclusion

For the purpose of optimal deep-learning-based segmentation purposes in potentially incomplete MRI examinations, the T1CE sequence should be acquired as early as possible.

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.