As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk ...
PURPOSE: To compare the diagnostic performance of different segmentations of the nerve fiber layer (NFL) thickness measurements using an artificial neural network and to define the optimal number of sectors with best diagnostic ability for glaucoma d...
OBJECTIVE: To explore the risk factors influencing the prognosis by analyzing clinical data of patients with acute paraquat intoxication, and to assess the prognostic values of acute physiology and chronic health evaluation II (APACHE II) score, sequ...
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
Nov 1, 2015
OBJECTIVES: To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data.
A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultras...
OBJECTIVE: To assess the value of passive leg raising (PLR) test in predicting fluid responsiveness in patients with sepsis-induced cardiac dysfunction.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Aug 1, 2015
Feature extraction is a fundamental step when mammography image analysis is addressed using learning based approaches. Traditionally, problem dependent handcrafted features are used to represent the content of images. An alternative approach successf...
PURPOSE: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using a...
PURPOSE: Detect and classify focal liver lesions (FLLs) from contrast-enhanced ultrasound (CEUS) imaging by means of an automated quantification algorithm.
Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; th...
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