Lung cancer remains the most common cause of cancer deaths in the world, but its mortality can be significantly reduced by diagnosis and early detection. Computerized resources were developed to assist radiologists in their management of the large vo...
In recent years, many new clinical diagnostic tools have been developed using complicated machine learning methods. Irrespective of how a diagnostic tool is derived, it must be evaluated using a 3-step process of deriving, validating, and establishin...
To better understand the capabilities and challenges of artificial intelligence and machine learning, we look at the role they can play in screening for retinopathy and colon cancer.
Metabolic syndrome and related disorders
Nov 1, 2019
We investigated the prevalence and the most relevant features of nonalcoholic steatohepatitis (NASH), a stage of nonalcoholic fatty liver disease, (NAFLD) in which the inflammation of hepatocytes can lead to increased cardiovascular risk, liver fibr...
OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Oct 25, 2019
Cardiac auscultation is the basic way for primary diagnosis and screening of congenital heart disease(CHD). A new classification algorithm of CHD based on convolution neural network was proposed for analysis and classification of CHD heart sounds in ...
Journal of burn care & research : official publication of the American Burn Association
Oct 16, 2019
We present in this paper the application of deep convolutional neural networks (CNNs), which is a state-of-the-art artificial intelligence (AI) approach in machine learning, for automated time-independent prediction of burn depth. Color images of fou...
OBJECTIVES: The objective of this study is to assess the performance of a computer-aided diagnosis (CAD) system (INTACT system) for the automatic classification of high-resolution computed tomography images into 4 radiological diagnostic categories a...
PURPOSE: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification.
We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review o...
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