AIMC Topic: ROC Curve

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A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images.

Scientific reports
Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method ...

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Journal of healthcare engineering
Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depe...

EMQIT: a machine learning approach for energy based PWM matrix quality improvement.

Biology direct
BACKGROUND: Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high...

Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve Based on Machine Learning for Risk Stratification of Non-Culprit Coronary Narrowings in Patients with Acute Coronary Syndrome.

The American journal of cardiology
This study investigated the prognostic value of coronary computed tomography angiography (cCTA)-derived fractional flow reserve (CT-FFR) in patients with acute coronary syndrome (ACS) and multivessel disease to gauge significance and guide management...

Protein asparagine deamidation prediction based on structures with machine learning methods.

PloS one
Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isom...

Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism.

Genetics in medicine : official journal of the American College of Medical Genetics
PurposeRecognizing individuals with inherited diseases can be difficult because signs and symptoms often overlap those of common medical conditions. Focusing on inborn errors of metabolism (IEMs), we present a method that brings the knowledge of high...

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning.

International journal of neural systems
In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The...

Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma.

Scientific reports
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in ...

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

PloS one
Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proacti...

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance.

Clinical science (London, England : 1979)
In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS ...