Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

Investigation Of The Effect Of Preparation Parameters On The Structural And Mechanical Properties Of Gelatin/Elastin/Sodium Hyaluronate Scaffolds Fabricated By The Combined Foaming And Freeze-Drying Techniques, Mansour Qamash, S. Misagh Imani, Meisam Omidi, Ciara Glancy, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

This paper aimed to evaluate the effects of different preparation parameters, including agitation speed, agitation time, and chilling temperature, on the structural and mechanical properties of a novel gelatin/elastin/sodium hyaluronate tissue engineering scaffold, recently developed by our research group. Fabricated using a combination of foaming and freeze-drying techniques, the scaffolds were assessed to understand how these parameters influence their morphology, internal microstructure, porosity, mechanical properties, and degradation behavior. The fabrication process used in this study involved preparing a homogeneous aqueous solution containing 8% gelatin, 2% elastin, and 0.5% sodium hyaluronate (w/v), which was then subjected to mechanical agitation at speeds …

Advancing Chronic Kidney Disease Prediction Through Machine Learning And Deep Learning With Feature Analysis, Shiddarth Dey Tusar, S. M. Ahad Ali Chowdhury, Md. Jalal Uddin Chowdhury, Rana M. Pir, H. M. Nur A. Alam, Muhammad Rezaur Rahman, Md. Nural Absar Siddiky, Muhammad Enayetur Rahman

Electrical & Computer Engineering Faculty Publications

Chronic Kidney Diesease (CKD) is a significant health issue, ranking as the fourth leading cause of mortality worldwide. The traditional diagnosis and treatment process, reliant on medical experts, is time-consuming. Therefore, thereis an urgent need for more efficient diagnostic methods to improve patient outcomes and reduce mortality rates. In this study, we employ Machine Learning (ML) and Deep Learning (DL) techniques to predict CKD based on important features. Feature analysis was performed using a correlation matrix and the LASSO algo-rithm to identify the most relevant features for model training. We evaluated several ML and DL classifiers, including Logistic Regression (LR), …

Integrated Machine Learning And Deep Learning Models For Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran

Electrical & Computer Engineering Faculty Publications

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

Image-To-Mesh Conversion Method For Multi-Tissue Medical Image Computing Simulations, Fotis Drakopoulos, Yixun Liu, Kevin Garner, Nikos Chrisochoides

Computer Science Faculty Publications

Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic mesh of high-quality elements. Next, the tetrahedral mesh is converted into a mixed element mesh of tetrahedra, pentahedra, and hexahedra to decrease element count while maintaining quality. Finally, the mesh surfaces are deformed to their corresponding physical image boundaries, improving the mesh’s fidelity. The deformation scheme builds upon the ITK open-source library …

Enhancing Heart Disease Prediction With Reinforcement Learning And Data Augmentation, Gayathri R., Sangeetha S. K. B., Sandeep Kumar Mathivanan, Hariharan Rajadurai, Benjula Anbu Malar Mb, Saurav Mallik, Hong Qin

Computer Science Faculty Publications

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation …

Enhanced Skin Cancer Diagnosis Through Grid Search Algorithm-Optimized Deep Learning Models For Skin Lesion Analysis, Rudresh Pillai, Neha Sharma, Sheifali Gupta, Deepali Gupta, Sapna Juneja, Saurav Malik, Hong Qin, Mohammed S. Alqahtani, Amel Ksibi

Computer Science Faculty Publications

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)-CNN1, CNN2, and CNN3-each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal …

Elucidating Mechanisms Of Enhanced Dsrna-Nanophytoglycogen Innate Immune Responses In Healthy Human Cells, Nicholas Jadaa

Theses and Dissertations (Comprehensive)

Immunostimulatory nucleic acids, such as long double-stranded RNA (ds)RNA, can stimulate innate immune responses in a non-sequence specific manner. These molecules are recognized by pattern-recognition receptors in the cytoplasm, endosome, and on the cell surface. Activation leads to the production of mediators of innate immune pathways, including type I interferon and proinflammatory cytokines. Although these receptors are present in all cells, their role in non-immune cells is often overlooked due to their lower level of responsiveness. Nanocarriers can enhance these nucleic acid-mediated responses, allowing for exploration of innate immune pathways in non-immune cells. Nanoparticles as carriers of nucleic acids can …

Implementing Best Practice Methods Of Endotracheal Tube Cuff Inflation In The Operating Room, Katherine Zeiger Brown

Graduate Theses, Dissertations, and Problem Reports (ETD)

Background: Use of manometers, inflating the endotracheal tube (ETT) cuff with a 5-mL syringe, displaying the pressure-volume loop (PV-L) on anesthesia machines, and auscultating with a stethoscope over the trachea have been shown effective in preventing ETT cuff overinflation in the operating room (OR). Purpose: The aims of this project were to implement and evaluate a best practice guideline for ETT cuff inflation in the OR. Methods: This project was conducted in several ORs at J.W. Ruby Memorial Hospital, in Morgantown, West Virginia. Participants included anesthesiologists, certified registered nurse anesthetists (CRNAs), student registered nurse anesthetists, residents, and medics. The ETT …

Improving Awareness And Identifying Risks For Emergence Delirium In Military Veterans, Hannah E. Pino

Graduate Theses, Dissertations, and Problem Reports (ETD)

Background: Emergence delirium (ED) is an acute state of confusion during the recovery phase from anesthesia that may be life-threatening, especially in the veteran population. Military veteran status may be explored by the Have You Ever Served questionnaire that was developed by the American Academy of Nursing to identify veterans upon admission to the hospital and thus be able to better care for them during their stay. This campaign has been in effect at the hospital site of this project for nearly 10 years. The questionnaire identifies military history risk factors such as combat history or a diagnosis of post-traumatic …

Twin Scholarships Of Glycomedicine And Precision Medicine In Times Of Single-Cell Multiomics, Seungyoul Oh, Weijie Cao, Manshu Song

Research outputs 2022 to 2026

Systems biology and multiomics research expand the prospects of planetary health innovations. In this context, this mini-review unpacks the twin scholarships of glycomedicine and precision medicine in the current era of single-cell multiomics. A significant growth in glycan research has been observed over the past decade, unveiling and establishing co- and post-translational modifications as dynamic indicators of both pathological and physiological conditions. Systems biology technologies have enabled large-scale and high-throughput glycoprofiling and access to data-intensive biological repositories for global research. These advancements have established glycans as a pivotal third code of life, alongside nucleic acids and amino acids. However, challenges …

Classification Of Vaginal Cleanliness Grades Through Surface-Enhanced Raman Spectral Analysis Via The Deep-Learning Variational Autoencoder: Long Short-Term Memory Model, Jia Wei Tang, Xin Ru Wen, Hui Min Chen, Jie Chen, Kun Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang

Research outputs 2022 to 2026

In this study, it is aimed to establish a novel method based on a deep-learning-guided surface-enhanced Raman spectroscopy (SERS) technique to achieve rapid and accurate classification of vaginal cleanliness levels. We proposed a variational autoencoder (VAE) approach to enhance spectral quality, coupled with a deep learning algorithm long short-term memory (LSTM) neural network to analyze SERS spectra produced by vaginal secretions. The performance of various machine learning (ML) algorithms is assessed using multiple evaluation metrics. Finally, the reliability of the optimal model is tested using blind test data (N = 10/group for each cleanliness level). The data quality of the …

Rapid Analysis Of N-Nitrosamines In Urine Using Ultra High-Pressure Liquid Chromatography-Mass Spectrometry, S. Shinde, K. D. Croft, J. M. Hodgson, C. P. Bondonno

Research outputs 2022 to 2026

N-Nitrosamines, carcinogenic compounds present in dietary and environmental sources and formed endogenously, are believed to be linked with the presence of nitrate and nitrite, both within dietary sources and after intake. To fully evaluate this potential threat to human health, an accurate analytical method to measure N-nitrosamines in biological matrices is necessary. We report a simple, fast, selective mass spectrometry method to detect N-nitrosamines in human urine. Analysis of seven N-nitrosamines, N-nitrosodimethylamine (NDMA), N-nitrosomethylethylamine (NMEA), N-nitrosodiethylamine (NDEA), N-nitrosopiperdine (NPIP), N-nitrosopyrrolidine (NPYR), N-nitrosodi-N-propylamine (NDPA) and N-nitrosodi-N-butylamine (NDBA) in urine was quantitated using Ultra High-Pressure Liquid Chromatography-tandem Mass spectrometry (UHPLC-MS/MS). A Sorbent …

Near-Infrared Paired-Agent Imaging For In Vivo Quantification Of Receptor Occupancy In Tumor, Yichen Feng

Dartmouth College Ph.D Dissertations

Accurate assessment of drug receptor occupancy (RO) holds significant importance in both drug development and personalized medicine, as it facilitates quantitative characterization of dose-response relationship for a given drug. This information not only aids appropriate dose selection for clinical trials but also guides personalized dose optimization in precision medicine.

Molecular imaging, utilizing receptor-specific imaging agents for tissue visualization, has emerged as the major option for in vivo measurement of tissue RO. Nonetheless, the abnormal yet complex structure and hemodynamics of tumors often lead to substantial non-specific uptake and retention of imaging agents, thus introducing significant bias to RO measurements. To …

Utilizing Ai Integrated Neuroimaging Technology To Expand Upon Machine Learning In Positron Emission Tomography Technology With The Aim Of Detecting Amyloid Beta Biomarkers Early In The Onset Of Alzheimer's., Ethan S. Terman

Undergraduate Research Posters

Early intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our day-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI …