Publications

Modeling microscopic cerebrovascular networks is essential for understanding cerebral blood flow and oxygen transport. High-resolution imaging modalities, such as Optical Coherence Tomography (OCT) and Two-Photon Microscopy (TPM), are widely used to capture microvascular structure and topology. Although TPM angiography generally provides better localization and image quality than OCT, its use is impractical in studies involving fluorescent dye leakage. Here, we exploit generative adversarial learning to produce high-quality TPM angiographies from OCT vascular stacks. We investigate the use of 2D and 3D cycle generative adversarial networks (CycleGANs) trained on unpaired image samples. We evaluate the generated TPM vascular structures based on image similarity and signal-to-noise ratio. Additionally, we evaluated the generated vascular structures after applying vessel segmentation and extracting their 3D topological models. Our results demonstrate that the 2D adversarial learning model outperforms the 3D model in terms of image quality. However, our statistical comparisons of vascular network features show the 3D model's consistent superiority in generating vascular structures. Our work provides a complementary approach to enhance vascular analysis when only OCT imaging is available.

The impact of Artificial Intelligence (AI) in healthcare is undeniable, aiding physicians in diagnosing diseases. This study aims to synthesize the literature to examine the progress of AI in diagnosing Tuberculosis (TB), one of the deadliest diseases in the world. The review also provides a taxonomy for AI-based studies for TB diagnosis. A scoping review approach was adopted using the PRISMA-Scoping guidelines, focusing on types of studies, focus area, algorithms/models, and key results. Relevant articles published from 2013 to 2022 were sought using PubMed, Web of Science, and Scopus, resulting in 199 articles included in the review. The use of AI, especially deep learning, has increased since 2016, particularly for diagnosing TB using chest X-Rays (CXR). Most studies focused on diagnosing TB using CXR, Computed Tomography, biomarkers, sputum smear, and drug resistance and recovery. Convolutional Neural Networks (CNNs) and their variants were commonly used in deep learning, while Support Vector Machine (SVM), Decision Trees, and ensemble algorithms performed well in machine learning. CNN outperformed other variants for CXRs (accuracy from 80 to 100%) probably due to their ability to handle high-dimensional data and extract features. In contrast, simpler algorithms like Naive Bayes underperformed compared to other algorithms (accuracy range: 79–89%), showing their limitations in dealing with complex TB diagnostic data. The use of AI approaches, namely machine and deep learning are expected to increase in the future, hence promising a rapid and cost-effective (and potentially sustainable) alternative solution for efficient TB diagnosis.

Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation. Objective: This scoping review will synthesize current literature and assess the use of various transformer models for neuroimaging segmentation. Methods: A systematic search in major databases, including Scopus, IEEE Xplore, PubMed, and ACM Digital Library, was carried out for studies applying transformers to neuroimaging segmentation problems from 2019 through 2023. The inclusion criteria allow only for peer-reviewed journal papers and conference papers focused on transformer-based segmentation of human brain imaging data. Excluded are the studies dealing with nonneuroimaging data or raw brain signals and electroencephalogram data. Data extraction was performed to identify key study details, including image modalities, datasets, neurological conditions, transformer models, and evaluation metrics. Results were synthesized using a narrative approach. Results: Of the 1246 publications identified, 67 (5.38%) met the inclusion criteria. Half of all included studies were published in 2022, and more than two-thirds used transformers for segmenting brain tumors. The most common imaging modality was magnetic resonance imaging (n=59, 88.06%), while the most frequently used dataset was brain tumor segmentation dataset (n=39, 58.21%). 3D transformer models (n=42, 62.69%) were more prevalent than their 2D counterparts. The most developed were those of hybrid convolutional neural network-transformer architectures (n=57, 85.07%), where the vision transformer is the most frequently used type of transformer (n=37, 55.22%). The most frequent evaluation metric was the Dice score (n=63, 94.03%). Studies generally reported increased segmentation accuracy and the ability to model both local and global features in brain images. Conclusions: This review represents the recent increase in the adoption of transformers for neuroimaging segmentation, particularly for brain tumor detection. Currently, hybrid convolutional neural network-transformer architectures achieve state-of-the-art performances on benchmark datasets over standalone models. Nevertheless, their applicability remains highly limited by high computational costs and potential overfitting on small datasets. The heavy reliance of the field on the brain tumor segmentation dataset hints at the use of a more diverse set of datasets to validate the performances of models on a variety of neurological diseases. Further research is needed to define the optimal transformer architectures and training methods for clinical applications. Continuing development may make transformers the state-of-the-art for fast, accurate, and reliable brain magnetic resonance imaging segmentation, which could lead to improved clinical tools for diagnosing and evaluating neurological disorders.

Pompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due to an enzyme deficiency, particularly harming the heart and muscles. Infantile-onset Pompe disease (IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the UAE to develop an expert rule-based screening approach operationalized through a dashboard. The study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were formulated to identify potential high-risk IOPD patients based on their age, particular symptoms, and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and specificity. The proposed approach accurately identified five true positives, one false negative, and four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA depletion syndrome 12-A (SLC25A4 gene), Immunodeficiency-71 (ARPC1B mutation), Niemann–Pick disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis. The proposed approach of integrating expert rules with a dashboard facilitated efficient data visualization and automated patient screening, which aids in the early detection of Pompe disease. Future studies are encouraged to investigate the application of machine learning methodologies to enhance further the precision and efficiency of identifying patients with IOPD.

In recent years, online reviews have become increasingly important in promoting various products and services. Unfortunately, writing deceptive reviews has also become a common practice to promote one's own business or tarnish the reputation of competitors. As a result, identifying fake reviews has become an intense and ongoing area of research. This paper proposes a node embedding approach to detect online fake reviews. The approach involves extracting features from the input data to create a distance matrix, which is then used to construct a Graph. From the graph, we extract graph nodes and use the Node2Vec biased random walk algorithm to create a model. We retrieve node embeddings from the Node2Vec model using Word2Vec and use different classifiers to classify the nodes. We trained and evaluated the machine learning models on the Deceptive Opinion Spam Corpus and YelpChi datasets and achieved superior results compared to prior work for detecting fake reviews, with SVM using the Hamming distance achieving 98.44% accuracy, 98.44% precision, 98.44% recall, and 98.44% F1-score. Furthermore, we explored different methods for explaining our proposed methods using explainable AI, demonstrating the interpretability of our approach. Our proposed node embedding approach shows promising results for detecting fake reviews and offers a transparent and interpretable solution for the problem.

This study proposes an innovative approach to the early identification of at-risk students in higher education settings by augmenting traditional machine learning classifiers with topological features and node embeddings derived from graph-based representations of student data from programming courses at the United Arab Emirates University. Utilizing a dataset comprising student demographic characteristics and course performance results, we construct an adjacency matrix using cosine and Canberra distance metrics, which is then thresholded to form a binary, unweighted graph. Topological features and node embeddings are extracted from this graph, representing the relationships and interactions between students. The extracted features are combined with the original dataset to train various machine learning classifiers, aiming to enhance their predictive accuracy. Experimental results demonstrate a significant improvement in prediction performance, with an increase of almost 10% in accuracy when node embeddings are incorporated. The most notable improvement was observed when using a Multi-Layer Perceptron classifier with the original dataset supplemented with both topological features and node embeddings, achieving 93.5% accuracy. Our findings highlight the potential of graph-based methods to enrich the feature set used by machine learning models, thereby enhancing their capacity to identify at-risk students early. Future work will focus on refining the feature extraction process, exploring other graph methods, and incorporating additional types of data. This study lays the foundation for more comprehensive and effective early-warning systems in higher education, aiming to enhance student support services and improve overall educational outcomes.

Computational quantification of magnetic resonance imaging (MRI) response from neurovascular structures is used to investigate potential biomarkers for different types of cerebrovascular deteriorations at the microscopic scale. Simulation-based MRI requires fully resolved microvascular structures, with geometric and physiological parameters, from tissue volumes captured using microscopic imaging modalities, e.g., optical coherence tomography (OCT). The preparation of such input models hinders large cohort studies and requires extensive manual effort. Here, we propose using 3D neural networks as an alternative learning-based solution over MRI simulation schemes. We trained state-of-the-art 3D neural networks to predict the spin echo (SE) MRI response from OCT microvascular volumes. By validating against simulated signals, our result demonstrates that the 3D ResNet-based regression network achieves a high accuracy to predict MRI signals with an average mean square error (MSE) <1%, R2 of 82.8% and explained variance score of 82.9%.

The rapid adoption of online learning in higher education has resulted in significant cybersecurity challenges. As educational institutions increasingly rely on digital platforms, they are facing cyber threats that can compromise sensitive data and disrupt operations. This systematic literature review explores the integration of artificial intelligence (AI) into traditional methods to address cybersecurity risks in online higher education. The review integrates a qualitative synthesis of relevant literature and a quantitative meta-analysis using PRISMA guidelines, ensuring comprehensive insights into the integration process. The most prevalent cybersecurity threats are examined, and the effectiveness of AI-based and conventional approaches in mitigating these challenges is compared. Additionally, the most effective AI techniques in cybersecurity solutions are analyzed, and their performance is compared across different contexts. Furthermore, the review considers the key ethical and technical considerations associated with integrating AI into traditional cybersecurity methods. The findings reveal that while AI-based techniques offer promising solutions for threat detection, authentication, and privacy preservation, their successful implementation requires careful consideration of data privacy, fairness, transparency, and robustness. The importance of interdisciplinary collaboration, continuous monitoring of AI models—by automated systems and humans—and the need for comprehensive guidelines to ensure responsible and ethical use of AI in cybersecurity are highlighted. The findings of this review provide actionable insights for educational institutions, educators, and students, helping to facilitate the development of secure and resilient online learning environments. The identified ethical and technical considerations can serve as a foundation for the responsible integration of AI into cybersecurity within the online higher-education sector.

Green space is any green infrastructure consisting of vegetation. Green space is linked with improving mental and physical health, providing opportunities for social interactions and physical activities, and aiding the environment. The quality of green space refers to the condition of the green space. Past machine learning-based studies have emphasized that littering, lack of maintenance, and dirtiness negatively impact the perceived quality of green space. These methods assess green spaces and their qualities without considering the human perception of green spaces. Domain-based methods, on the other hand, are labour-intensive, time-consuming, and challenging to apply to large-scale areas. This research proposes to build, evaluate, and deploy a machine learning methodology for assessing the quality of green space at a human-perception level using transfer learning on pre-trained models. The results indicated that the developed models achieved high scores across six performance metrics: accuracy, precision, recall, F1-score, Cohen’s Kappa, and Average ROC-AUC. Moreover, the models were evaluated for their file size and inference time to ensure practical implementation and usage. The research also implemented Grad-CAM as means of evaluating the learning performance of the models using heat maps. The best-performing model, ResNet50, achieved 98.98% accuracy, 98.98% precision, 98.98% recall, 99.00% F1-score, a Cohen’s Kappa score of 0.98, and an Average ROC-AUC of 1.00. The ResNet50 model has a relatively moderate file size and was the second quickest to predict. Grad-CAM visualizations show that ResNet50 can precisely identify areas most important for its learning. Finally, the ResNet50 model was deployed on the Streamlit cloud-based platform as an interactive web application.

Cardiovascular diseases (CVDs) are the leading cause of death globally, with millions of lives lost yearly. CVDs are a group of disorders of the heart and blood vessels. Although there are no exact causes of CVDs, there are risk factors associated that increase the likelihood of getting CVDs. Clustering and association rule mining are among the methods used for pattern discovery. However, not much research has been proposed to compare clustering and association algorithms regarding risk factors of CVDs. Hence, this study presents a comparative analysis of clustering and association on the risk factors of CVDs to assess which factors are significant. The Framingham Heart Study dataset was used for clustering and association rule mining. The clustering results using three clusters show that older age, high BMI, and high systolic blood pressure are the significant risk factors. Smoking and hypertension are among the risk factors contributing to angina and heart attack based on association analysis with minimum support, minimum confidence, and maximum items of 25%, 60% and 4, respectively. This study successfully adopted clustering and association for pattern discovery to assess the most critical risk factors of CVDs.