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| Artificial neural network with a single hidden layer. There is complete connection between layers. Blue circles: Input layer. Red circles: Hidden layer. Yellow circle: Output layer.
Source publication
Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transf...
Context in source publication
Context 1
... network can be trained (e.g., to use different weighting schemes) to fit a particular dataset by the use of learning algorithms. The ANN is very flexible for handling different types of data, but it is prone to data-overfitting and requires vast computational resources (Figure 4) (10). Another important disadvantage to neural network approaches is the lack of transparency in the FIGURE 2 | A hyperplane separating two classes of data points in 2D space (A, blue line). ...
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Citations
... In another study, AI-enabled human organchip systems create patient-specific medicine. 59 Likewise, the study 60 found that deep neural network (DNN) methods perform outstandingly in the personalised medication of Gilomas. Other factors, such as digitisation of data, efficiency, cost saving, and effective data processing with optimal results, are the reason for the growth of research in the field of AI in healthcare. ...
The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include ‘ML’, ‘Deep Learning’, and ‘Artificial Intelligence’.
... The pace of AI advancements in these areas suggests what might be possible in genomics if done properly. While the potential of AI for genomic discovery is tremendous, there is also significant potential to build AI models that utilize genomic data to make complex genetic diagnoses, 4,5 personalize treatment plans, 6,7 and identify and prevent health risks. 8 The inherent complexities of genomic data including large dimensionality and lack of prior knowledge regarding the functional aspects of most genomic variants present major challenges for AI application. ...
Objective
Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association—Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.
Process
A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.
Conclusions
Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.
... LUTDs are diagnosed based on the tendency and some special values, such as maximum and minimum. Timesequential events have been shown to be detectable using machine learning, such as decision trees, quadratic support vector machines, logistic regression, and fast Fourier transforms [7]. Utilizing artificial intelligence techniques to assist in interpreting UDS could reduce heterogeneity in manual interpretation by standardizing automatic diagnosis and further promote the popularity and use of urodynamic practice, especially in general or community hospitals. ...
To establish an automatic diagnostic system based on machine learning for preliminarily analysis of urodynamic study applying in lower urinary tract dysfunction (LUTD).
The eight most common conditions of LUTDs were included in the present study. A total of 527 eligible patients with complete data, from the year of 2015 to 2020, were enrolled in this study. In total, two global parameters (patients’ age and sex) and 13 urodynamic parameters were considered to be the input for machine learning algorithms. Three machine learning approaches were applied and evaluated in this study, including Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM).
By applying machine learning algorithms into the 8 common LUTDs, the DT models achieved the AUC of 0.63–0.98, the LR models achieved the AUC of 0.73–0.99, and the SVM models achieved the AUC of 0.64–1.00. For mutually exclusive diagnoses of underactive detrusor and acontractile detrusor, we developed a classification model that classifies the patients into either of these two diseases or double-negative class. For this classification method, the DT models achieved the AUC of 0.82–0.85 and the SVM models achieved the AUC of 0.86–0.90. Among all these models, the LR and the SVM models showed better performance. The best model of these diagnostic tasks achieved an average AUC of 0.90 (0.90 ± 0.08).
An automatic diagnostic system was developed using three machine learning models in urodynamic studies. This automated machine learning process could lead to promising assistance and enhancements of diagnosis and provide more useful reference for LUTD treatment.
... Linear and logistic regression, support vector machines (SVMs), Naive Bayes (NB), decision trees (DTs) and random forest (RF) methods are the most used SVM techniques. [41] Unsupervised learning is not trained by using training data. Instead, unsupervised data finds hidden layers from the data. ...
... SVM is a supervised learning algorithm, which is trained on human-labelled datasets and then use for classification into categories on unlabelled datasets. [41,42] SVM is developed by Vapnik and Lerner in the year 1963. In brain tumour diagnosis, it is used for tumour detection, segmentation and also for the classification of tumours. ...
... Most diagnostic protocols in a clinical environment rely on imaging-based tests for authentic health disorders. However, clinicians interpret this imagery data manually, and where required, other pathologies or microbiologybased tests are recommended [1]. These tests are not only time-consuming but require surgical processes also. ...
Glioma is the most occurring brain tumor in the world. Its grade (level of severity) identification, crucial in its treatment planning, is most demanding in a clinical environment. Computer-aided methods have been experimented with to identify the grade of glioma, out of which deep learning-based methods, due to their auto features engineering, have a good impact in terms of their achieved outcomes. In this study, convolutional neural networks (CNNs) have been explored and utilized for the classification of glioma grading, for example, low grade (grade I-II) and high grade (grade III-IV). A CNN-based model, which is light-weighted in terms of layers, size, and learnable parameters, has been proposed. Experimental tests were carried out on benchmarked publicly available datasets, for example, Brats-2017, Brats-2018, & Brats-2019. A locally developed dataset from Bahawal Victoria Hospital, Bahawalpur, Pakistan, has also been employed for experimentation and research to cross-validate the outcomes. Additionally, experiments have been carried out to compare the effectiveness of the proposed model, and results have been compared with the results of state-of-the-art pertained CNN models, i.e., resnet18, squeezenet, and alexnet. The proposed model achieved maximum standard evaluation measures on the benchmarked dataset, i.e., accuracy, specificity, and sensitivity at 97.85%, 98.88%, and 99.88%, respectively. Similarly, these measures were 98.89%, 99.28%, and 99.77% on a locally developed dataset, which is the best compared to the recent state-of-the-art related studies.
... Sotoudeh et al. have recently reviewed AI techniques in the context of brain tumor imaging. 182 In short, supervised and unsupervised machine learning and different neural network techniques can be used to train algorithms via a series of known values (e.g., imaging, histological, genetic, and clinical substrates in variable combinations) thereby facilitating the prediction outcomes based on initial training datasets. ...
Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. Additionally, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.
... The disease is characterized by intense cell proliferation, diffuse infiltration, aggressive progression and other malignant features (Furnari et al. 2007). Despite improvements in surgical, radiotherapy and chemotherapy strategies, the 5-year survival rate for glioma patients remains poor (Sotoudeh et al. 2019;Bush et al. 2017). Therefore, further elucidation of the mechanism of glioma development will offer clues for the diagnosis and treatment of glioma. ...
To explore the functions of circRNA cyclin B1 (circCCNB1) in glioma and its possible mechanisms. The expression of circCCNB1, eukaryotic translation initiation factor 4A3 (EIF4A3), cyclin D1 (CCND1) and miR-516b-5p was determined by qRT-PCR, western blot or immunohistochemistry (IHC) assay. The feature of circCCNB1 was analyzed by Actinomycin D (ActD), RNase R and subcellular fraction assays. The molecule relationships were analyzed by RIP, dual-luciferase reporter and RNA pull-down assays. CCK-8, EdU and colony formation assays were performed to analyze cell proliferation. Flow cytometry analysis was executed to estimate the cell cycle. Murine xenograft model assay was used for the role of circCCNB1 in vivo. CircCCNB1 was overexpressed in glioma tissues and cells. EIF4A3 positively regulated circCCNB1 expression. CircCCNB1 knockdown repressed glioma cell proliferation and cell cycle process in vitro and blocked tumor growth in vivo. CircCCNB1 knockdown reduced CCND1 expression in glioma cells and CCND1 overexpression bated the effect of circCCNB1 knockdown on glioma cell growth. CircCCNB1 interacted with HuR to elevate CCND1 expression. miR-516b-5p could interact with circCCNB1 and CCND1. CircCCNB1 regulated glioma cell progression and CCND1 expression by miR-516b-5p and HuR. CircCCNB1 aggravated glioma cell growth by elevating CCND1 through targeting miR-516b-5p and HuR.
... Supervised learning itself can be achieved through several techniques such as support vector matrixes (SVMs) which aim to delineate a function that separates two sets of data. [33] Previously, studies have demonstrated that gliomas can be classified according to their clinical grade using linear SVMs which were trained on descriptive features such as amount of mass effect of blood supply. e limitation of these studies, such as one by Li et al., is that these quantitative features were estimated by domain experts and the definition of these features was limited to expert opinion and hence not reproducible. ...
Deep learning (DL) is a relatively newer subdomain of machine learning (ML) with incredible potential for certain applications in the medical field. Given recent advances in its use in neuro-oncology, its role in diagnosing, prognosticating, and managing the care of cancer patients has been the subject of many research studies. The gamut of studies has shown that the landscape of algorithmic methods is constantly improving with each iteration from its inception. With the increase in the availability of high-quality data, more training sets will allow for higher fidelity models. However, logistical and ethical concerns over a prospective trial comparing prognostic abilities of DL and physicians severely limit the ability of this technology to be widely adopted. One of the medical tenets is judgment, a facet of medical decision making in DL that is often missing because of its inherent nature as a “black box.” A natural distrust for newer technology, combined with a lack of autonomy that is normally expected in our current medical practices, is just one of several important limitations in implementation. In our review, we will first define and outline the different types of artificial intelligence (AI) as well as the role of AI in the current advances of clinical medicine. We briefly highlight several of the salient studies using different methods of DL in the realm of neuroradiology and summarize the key findings and challenges faced when using this nascent technology, particularly ethical challenges that could be faced by users of DL.
... Computer science leading the incorporation of artificial intelligence into clinical nomograms to generate integrative systems for medicine is changing our approaches to analyze large-scale and complex datasets. Machine learning algorithms are already changing the field of cancer diagnosis and prognosis by exploring diverse data types, including imaging, histology, and multiomics, to efficiently classify various clinically relevant GBM traits [85][86][87][88]. These advances will undoubtedly bring novel and more personalized diagnostic and therapeutic alternatives for patients with GBM. ...
Glioblastoma (GBM) is the most aggressive primary brain tumor, having a poor prognosis and a median overall survival of less than two years. Over the last decade, numerous findings regarding the distinct molecular and genetic profiles of GBM have led to the emergence of several therapeutic approaches. Unfortunately, none of them has proven to be effective against GBM progression and recurrence. Epigenetic mechanisms underlying GBM tumor biology, including histone modifications, DNA methylation, and chromatin architecture, have become an attractive target for novel drug discovery strategies. Alterations on chromatin insulator elements (IEs) might lead to aberrant chromatin remodeling via DNA loop formation, causing oncogene reactivation in several types of cancer, including GBM. Importantly, it is shown that mutations affecting the isocitrate dehydrogenase (IDH) 1 and 2 genes, one of the most frequent genetic alterations in gliomas, lead to genome-wide DNA hypermethylation and the consequent IE dysfunction. The relevance of IEs has also been observed in a small population of cancer stem cells known as glioma stem cells (GSCs), which are thought to participate in GBM tumor initiation and drug resistance. Recent studies revealed that epigenomic alterations, specifically chromatin insulation and DNA loop formation, play a crucial role in establishing and maintaining the GSC transcriptional program. This review focuses on the relevance of IEs in GBM biology and their implementation as a potential theranostic target to stratify GBM patients and develop novel therapeutic approaches. We will also discuss the state-of-the-art emerging technologies using big data analysis and how they will settle the bases on future diagnosis and treatment strategies in GBM patients.
... Despite the development of various comprehensive therapies, the therapeutic effects are still not satisfactory [3]. The average overall survival of patients with glioblastoma (GBM), the most malignant type of glioma, remains approximately 12 to 15 months [4,5]. Therefore, in-depth studies of the mechanisms involved in molecular regulation and biological function in glioma progression are needed to develop more effective treatment strategies. ...
Purpose: Several studies have indicated that SLC39A7 plays an important role in tumor progression; however, little is known about the function and mechanism of SLC39A7 in glioma. In this study, we aimed to explore the role of SLC39A7 in glioma development.
Patients and methods: Bioinformatic analysis was used to predict the role of SLC39A7 in glioma. Cell viability and Edu assays were used to detect the proliferation of glioma cells. A transwell assay was used to measure the invasion and migration of glioma cells. Western blotting, qPCR and ELISA were used to detect the expression of all molecules.
Results: SLC39A7 was found to be highly expressed in high-grade glioma patients with a poor prognosis. Our results indicated that SLC39A7 significantly promoted the proliferation, invasion and migration of glioma cells. Furthermore, SLC39A7 promoted tumorigenesis in orthotopic models. We determined that SLC39A7 promotes the malignant behaviors of glioma by activating the TNF-α-mediated NF-κB signaling pathway.
Conclusion: Our study revealed that SLC39A7 promotes the proliferation, invasion and migration of glioma cells via the TNF-α-mediated NF-κB signaling pathway, which provides potential targets for glioma therapy.
