Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Engineering
Metabolite Biomarker Discovery For Lung Cancer Using Machine Learning, Ariski Fajarido, Linda Erlina, Aryo Tedjo, Fadilah Fadilah, Wawaimuli Arozal
Metabolite Biomarker Discovery For Lung Cancer Using Machine Learning, Ariski Fajarido, Linda Erlina, Aryo Tedjo, Fadilah Fadilah, Wawaimuli Arozal
Indonesian Journal of Medical Chemistry and Bioinformatics
Lung cancer is the leading cause of cancer death worldwide. About 2.1 million lung cancer patients were diagnosed in 2018, accounting for about 11.6% of all newly diagnosed cancer cases. For lung cancer, blood is the first choice as a source of screening biomarker candidates. Blood biomarkers provide a snapshot of the patient's entire body, including the primary tumor, metastatic disease, immune response, and peritumoral stroma. However, sputum sampling, bronchial lavage or aspiration, exhaled breath (EB), and airway epithelial sampling represent unique samples for lung cancer and other airway cancers as potential sources for alternative biomarkers. Metabolites are products of …
Data Preprocessing And Machine Learning For Intracranial Electroencephalography, Mauricio Cespedes Tenorio
Data Preprocessing And Machine Learning For Intracranial Electroencephalography, Mauricio Cespedes Tenorio
Electronic Thesis and Dissertation Repository
This thesis serves to address the problem of non-standardized preprocessing of intracranial electroencephalography (iEEG) recordings by implementing a software workflow that compiles some of the most common steps followed for the preparation of this type of data. This workflow improves the consistency, replicability, and ease of use of iEEG preprocessing, facilitating the replication and extension of previous studies and the combination of separately preprocessed inter-institutional datasets. Automatic detection of artifacts for iEEG data was also explored as a potential step to include in the preprocessing workflow. Despite training the models with cross-institutional data, poor performance was observed when tested on …
Evaluating Neuroimaging Modalities In The A/T/N Framework: Single And Combined Fdg-Pet And T1-Weighted Mri For Alzheimer’S Diagnosis, Peiwang Liu
McKelvey School of Engineering Theses & Dissertations
With the escalating prevalence of dementia, particularly Alzheimer's Disease (AD), the need for early and precise diagnostic techniques is rising. This study delves into the comparative efficacy of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and T1-weighted Magnetic Resonance Imaging (MRI) in diagnosing AD, where the integration of multimodal models is becoming a trend. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we employed linear Support Vector Machines (SVM) to assess the diagnostic potential of these modalities, both individually and in combination, within the AD continuum. Our analysis, under the A/T/N framework's 'N' category, reveals that FDG-PET consistently outperforms T1w-MRI across …
Database And Machine Learning Model For Classifying Autism Spectrum Disorder From Smartphone Based Electroretinography, Rory Harris
Honors Scholar Theses
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that negatively affects a patient’s cognitive and communication aptitude and, therefore, can severely impact that patient’s quality of life. Because of this, early diagnosis is paramount. In recent studies, electroretinography (ERG), which is a measure of the retina’s electrical response to a brief flash of light into the eye, has shown promise in detecting ASD. Access to these scans can provide early diagnosis, improving well-being. Current ERG devices are very expensive due to their on board processing capabilities. This paper aims to create an ERG device using a smartphone as the main …
Temporospatial Deep Learning Strategies For Prediction Of Disease Progression In Radiology, John D. Mayfield
Temporospatial Deep Learning Strategies For Prediction Of Disease Progression In Radiology, John D. Mayfield
USF Tampa Graduate Theses and Dissertations
While the fields of machine learning and medicine are deeply rooted in axiomatic scientific principles, there is an element of art that makes the practice imperfect, yet innately human. As the two fields have seen the greatest overlap in their collective history, there remains a chasm between them in terms of practical translation for the patients who desire and deserve personalized medicine. As presented in this dissertation, I and my collaborators have contributed to the groundwork for future exploration of predicting disease progression by identifying signals within sequential medical imaging to provide a temporospatial relationship upon which we can make …
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Theses and Dissertations
Bone marrow lesions (BMLs), occurs from fluid build up in the soft tissues inside your bone. This can be seen on magnetic resonance imaging (MRI) scans and is characterized by excess water signals in the bone marrow space. This disease is commonly caused by osteoarthritis (OA), a degenerative join disease where tissues within the joint breakdown over time [1]. These BMLs are an emerging target for OA, as they are commonly related to pain and worsening of the diseased area until surgical intervention is required [2]–[4]. In order to assess the BMLs, MRIs were utilized as input into a regression …