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Artificial Intelligence in Digestive Endoscopy—Where Are We and Where Are We Going?
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In: Diagnostics; Volume 12; Issue 4; Pages: 927 (2022)
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Toward quantitative assessment of deformational plagiocephaly and brachycephaly at the point-of-care
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In: J Med Imaging (Bellingham) (2021)
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Development of Gaussian Learning Algorithms for Early Detection of Alzheimer's Disease
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In: FIU Electronic Theses and Dissertations (2020)
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Abstract:
Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer's Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot be reversed or stopped. This research aims to develop Gaussian learning algorithms, natural language processing (NLP) techniques, and mathematical models to effectively delineate the MCI participants from the cognitively normal (CN) group, and identify the most significant brain regions and patterns of changes associated with the progression of AD. The focus will be placed on the earliest manifestations of the disease (early MCI or EMCI) to plan for effective curative/therapeutic interventions and protocols. Multiple modalities of biomarkers have been found to be significantly sensitive in assessing the progression of AD. In this work, several novel multimodal classification frameworks based on proposed Gaussian Learning algorithms are created and applied to neuroimaging data. Classification based on the combination of structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers is seen as the most reliable approach for high-accuracy classification. Additionally, changes in linguistic complexity may provide complementary information for the diagnosis and prognosis of AD. For this research endeavor, an NLP-oriented neuropsychological assessment is developed to automatically analyze the distinguishing characteristics of text data in MCI group versus those in CN group. Early findings suggest significant linguistic differences between CN and MCI subjects in terms of word usage, vocabulary, recall, fragmented sentences. In summary, the results obtained indicate a high potential of the neuroimaging-based classification and NLP-oriented assessment to be utilized as a practically computer aided diagnosis system for classification and prediction of AD and its prodromal stages. Future work will ultimately focus on early signs of AD that could help in the planning of curative and therapeutic intervention to slow the progression of the disease.
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Keyword:
Alzheimer’s disease; Biomedical; computer-aided diagnosis; Diagnosis; Diagnostic and Therapeutic Techniques and Equipment; dimensionality reduction; mild cognitive impairment (MCI); multiclass classification; multimodal analysis; Multivariate Analysis; natural language processing (NLP); Other Analytical; Other Biomedical Engineering and Bioengineering; Signal Processing; Statistical Models; Theory and Algorithms
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URL: https://digitalcommons.fiu.edu/etd/4398 https://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=5672&context=etd
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Artificial Intelligence and its future potential in lung cancer screening
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Biomedical applications of voice and speech processing ; Aplicaciones biomédicas del procesamiento de la voz y el habla
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In: Loquens; Vol. 4 No. 1 (2017); e035 ; Loquens; Vol. 4 Núm. 1 (2017); e035 ; 2386-2637 ; 10.3989/loquens.2017.v4.i1 (2017)
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Coronary artery analysis: Computerâ assisted selection of bestâ quality segments in multipleâ phase coronary CT angiography
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A Hybrid Approach to Clinical Question Answering
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In: DTIC (2014)
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Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms
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Representational and architectural issues in a limited-domain medical speech translator
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In: TALN-RECITAL 2005, Traitement Automatique des Langues Naturelles pp. 163-172 (2005)
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Eliciting a terminology for mammographic calcifications
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In: CLIN RADIOL , 57 (11) 1007 - 1013. (2002) (2002)
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Computer Aided Breast Cancer Diagnosis
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In: DTIC AND NTIS (2000)
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Breast Ultrasound Computer-Aided Diagnosis Approach to Improving Specificity and Decreasing Observer Variability
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In: DTIC AND NTIS (1999)
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Breast Ultrasound: Computer-Aided Diagnosis Approach to Improving Specificity and Decreasing Observer Variability
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In: DTIC AND NTIS (1998)
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Increasing the Accuracy of Mammogram Interpretation
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In: DTIC AND NTIS (1998)
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Computer Aided Breast Cancer Diagnosis.
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In: DTIC AND NTIS (1996)
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Vocal Cord Function and Voice Quality Evaluation of Active Duty U.S. Army Drill Instructors.
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In: DTIC AND NTIS (1996)
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Extending the Rule Space Model to a Semantically-Rich Domain: Diagnostic Assessment in Architecture.
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In: DTIC AND NTIS (1993)
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Talking to InterFIS: Adding Speech Input to a Natural Language Interface
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In: DTIC AND NTIS (1992)
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Computerized Assessment of Individual Differences
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In: DTIC AND NTIS (1991)
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MUMPS Based Integration of Disparate Computer-Assisted Medical Diagnosis Modules
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In: DTIC AND NTIS (1989)
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