Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to t...
BACKGROUND: There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previ...
We developed an unsupervised machine learning algorithm and applied it to big corneal parameters to identify and monitor keratoconus stages. A big dataset of corneal swept source optical coherence tomography (OCT) images of 12,242 eyes acquired from ...
IEEE transactions on bio-medical engineering
Nov 5, 2018
As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disease (PD) usi...
Journal of diabetes and its complications
Nov 3, 2018
AIMS: Serum uric acid (UA) increases in patients with kidney disease due to the impaired UA clearance. The present study sought to evaluate the association between UA/creatinine ratio (UA/Cr) and renal disease progression in patients with type 2 diab...
Medical & biological engineering & computing
Nov 3, 2018
This paper presents a unified approach based on the recurrence quantification analysis (RQA) and approximate entropy (ApEn) for the classification of heart rate variability (HRV). In this paper, the optimum tolerance threshold (r) corresponding to Ap...
AIMS: Recent studies have implicated possible contribution of adipocytokines in development and progression of microvascular complications in patients with type 1 diabetes (T1DM). The aim of our study was to investigate relationship between adipocyto...
OBJECTIVE: This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features.
IEEE journal of biomedical and health informatics
Nov 1, 2018
Quantitative analysis of the heart is extremely necessary and significant for detecting and diagnosing heart disease, yet there are still some challenges. In this study, we propose a new end-to-end segmentation-based deep multi-task regression learni...
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects. Multimodality neuroimagi...
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