AIMC Topic: Algorithms

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Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

Acta neurologica Scandinavica
OBJECTIVES: Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates befo...

Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm.

Toxicologic pathology
In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate a...

A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.

Journal of biomedical informatics
BACKGROUND: Machine learning methodologies are gaining popularity for developing medical prediction models for datasets with a large number of predictors, particularly in the setting of clustered and longitudinal data. Binary Mixed Model (BiMM) fores...

A deep convolutional neural network to simultaneously localize and recognize waste types in images.

Waste management (New York, N.Y.)
Accurate waste classification is key to successful waste management. However, most current studies have focused exclusively on single-label waste classification from images, which goes against common sense. In this paper, we move beyond single-label ...

Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data.

European journal of cancer (Oxford, England : 1990)
PURPOSE: Several multi-omics classifications have been proposed for hepato-pancreato-biliary (HPB) cancers, but these classifications have not proven their role in the clinical practice and been validated in external cohorts.

A deep cascaded segmentation of obstructive sleep apnea-relevant organs from sagittal spine MRI.

International journal of computer assisted radiology and surgery
PURPOSE: The main purpose of this work was to develop an efficient approach for segmentation of structures that are relevant for diagnosis and treatment of obstructive sleep apnea syndrome (OSAS), namely pharynx, tongue, and soft palate, from mid-sag...

Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach.

Sensors (Basel, Switzerland)
Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. T...

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier.

Sensors (Basel, Switzerland)
Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle de...

Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring.

Communications biology
Approaches to reliably predict the developmental potential of embryos and select suitable embryos for blastocyst culture are needed. The development of time-lapse monitoring (TLM) and artificial intelligence (AI) may help solve this problem. Here, we...

Proposal of a novel Artificial Intelligence Distribution Service platform for healthcare.

F1000Research
In this paper, we focus on presenting a novel AI-based service platform proposal called AIDI (Artificial Intelligence Distribution Interface for healthcare). AIDI proposal is based on our earlier research work in which we evaluated AI-based healthcar...