Ai-Driven Approach For Diagnosis Of Renal Transplant Rejection Based On Biomarkers Identification And Integration., Israa Sharaby

Electronic Theses and Dissertations

They kidney is a vital organ for which humans are fortunate to find a spare through transplantation to sustain critical body functions, offering a hope to those struggling with renal failure. Kidney transplant procedure is the optimal treatment for people who suffer from end-stage renal failure. However, there are posed challenges due to the risk of immune rejection and the limited availability of donors. Early detection of renal rejection can provide timely intervention and accurate diagnosis that are critical to improve the transplant outcomes. This study explores the innovative approaches for addressing the current challenges through biomarkers identification, imaging techniques, …

Automated Segmentation Of Autofluorescence Lifetime Images Using A Convolutional Neural Network, Nicole Hooten

Biomedical Engineering Undergraduate Honors Theses

Chronic wounds affect nearly 1 out of every 50 people in the United States, decreasing quality of life and putting people with potential comorbidities at high risk for obtaining an infection. Wound healing progress in clinical settings is measured by tracking wound size and there are currently no non-invasive, quantitative measurement techniques. Previous studies have proposed nicotinamide adenine dinucleotide (NADH) autofluorescence lifetime (FLIM) imaging of the wound edge as a method to quantify the wound healing process by connecting the role of NADH in cellular metabolism to wound healing stage. However, evaluation of FLIM images is heavily subjective, as a …

Efficient Scopeformer: Towards Scalable And Rich Feature Extraction For Intracranial Hemorrhage Detection Using Hybrid Convolution And Vision Transformer Networks, Yassine Barhoumi

Theses and Dissertations

The field of medical imaging has seen significant advancements through the use of artificial intelligence (AI) techniques. The success of deep learning models in this area has led to the need for further research. This study aims to explore the use of various deep learning algorithms and emerging modeling techniques to improve training paradigms in medical imaging. Convolutional neural networks (CNNs) are the go-to architecture for computer vision problems, but they have limitations in mapping long-term dependencies within images. To address these limitations, the study explores the use of techniques such as global average pooling and self-attention mechanisms. Additionally, the …

Cbct In Clinical Practice, Tarunjeet Pabla Bds, Dmd, Ms, Dip. Abomr, Hugo C. Campos Dds, Dmd, Mds, Dip. Abomr, Aruna Ramesh Bds, Dmd, Ms, Dip. Abomr

The Journal of the Michigan Dental Association

This feature explores the integration of Cone Beam Computed Tomography (CBCT) into dental practice, offering guidelines for best practices. Introduced in 2001, CBCT revolutionized dental radiography, impacting various clinical areas. The article emphasizes the need for clinicians to comprehend CBCT technology, its benefits, and potential risks. It delves into CBCT imaging considerations, technical parameters (Field of View, Voxel Size, Spatial and Contrast Resolution), image viewing, artifacts, machine calibration, and service. Addressing radiation dose, risks, and protection, the article outlines decision-making for 2D vs. 3D imaging. It underscores the responsibility of interpreting CBCT images, legal considerations, return on investment, and the …

Artificial Image Objects For Classification Of Breast Cancer Biomarkers With Transcriptome Sequencing Data And Convolutional Neural Network Algorithms, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko, Jingchun Chen

School of Medicine Faculty Research

Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods: We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = …

A Brief Bibliometric Survey Of Explainable Ai In Medical Field, Nilkanth Mukund Deshpande, Shilpa Shailesh Gite

Library Philosophy and Practice (e-journal)

_{^{Background: This study aims to analyze the work done in the field of explainability related to artificial intelligence, especially in the medical field from 2004 onwards using the bibliometric methods.}}

_{^{Methods: different articles based on the topic leukemia detection were retrieved using one of the most popular database- Scopus. The articles are considered from 2004 onwards. Scopus analyzer is used for different types of analysis including documents by year, source, county and so on. There are other different analysis tools such as VOSviewer Version 1.6.15. This is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis …}}

Imparting 3d Representations To Artificial Intelligence For A Full Assessment Of Pressure Injuries., Sofia Zahia

Electronic Theses and Dissertations

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning …

Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj

Electrical & Computer Engineering Faculty Research

The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are …