Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Medical Biotechnology
Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virigina B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai
Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virigina B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai
Electrical & Computer Engineering Faculty Research
Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications …
The Contribution Of Ethical Governance Of Artificial Intelligence & Machine Learning In Healthcare, Tina Nguyen
The Contribution Of Ethical Governance Of Artificial Intelligence & Machine Learning In Healthcare, Tina Nguyen
Electronic Theses and Dissertations
With the Internet Age and technology progressively advancing every year, the usage of Artificial Intelligence (AI) along with Machine Learning (ML) algorithms has only increased since its introduction to society. Specifically, in the healthcare field, AI/ML has proven to its end-users how beneficial its assistance has been. However, despite its effectiveness and efficiencies, AI/ML has also been under scrutiny due to its unethical outcomes. As a result of this, two polarizing views are typically debated when discussing AI/ML. One side believes that AI/ML usage should continue regardless of its unsureness, while the other side argues that this technology is too …
A Conformation Variant Of P53 Combined With Machine Learning Identifies Alzheimer Disease In Preclinical And Prodromal Stages, Giulia Abate, Marika Vezzoli, Letizia Polito, Antonio Guaita, Diego Albani, Moira Marizzoni, Emirena Garrafa, Alessandra Marengoni, Gianluigi Forloni, Giovanni B. Frisoni, Jeffrey L. Cummings, Maurizio Memo, Daniela Uberti
A Conformation Variant Of P53 Combined With Machine Learning Identifies Alzheimer Disease In Preclinical And Prodromal Stages, Giulia Abate, Marika Vezzoli, Letizia Polito, Antonio Guaita, Diego Albani, Moira Marizzoni, Emirena Garrafa, Alessandra Marengoni, Gianluigi Forloni, Giovanni B. Frisoni, Jeffrey L. Cummings, Maurizio Memo, Daniela Uberti
School of Medicine Faculty Research
© 2020 by the authors. Li-censee MDPI, Basel, Switzerland. Early diagnosis of Alzheimer’s disease (AD) is a crucial starting point in disease man-agement. Blood-based biomarkers could represent a considerable advantage in providing AD-risk information in primary care settings. Here, we report new data for a relatively unknown blood-based biomarker that holds promise for AD diagnosis. We evaluate a p53-misfolding conformation rec-ognized by the antibody 2D3A8, also named Unfolded p53 (U-p532D3A8+), in 375 plasma samples derived from InveCe.Ab and PharmaCog/E-ADNI longitudinal studies. A machine learning approach is used to combine U-p532D3A8+ plasma levels with Mini-Mental State Examination (MMSE) and apolipoprotein E …