AIMC Topic:
ROC Curve

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Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.

PloS one
OBJECTIVE: Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to...

Developing neural network models for early detection of cardiac arrest in emergency department.

The American journal of emergency medicine
BACKGROUND: Automated surveillance for cardiac arrests would be useful in overcrowded emergency departments. The purpose of this study is to develop and test artificial neural network (ANN) classifiers for early detection of patients at risk of cardi...

Weakly supervised lesion localization for age-related macular degeneration detection using optical coherence tomography images.

PloS one
Age-related macular degeneration (AMD) is the main cause of irreversible blindness among the elderly and require early diagnosis to prevent vision loss, and careful treatment is essential. Optical coherence tomography (OCT), the most commonly used im...

Development of a Strategy to Predict and Detect Falls Using Wearable Sensors.

Journal of medical systems
Falls are a prevalent problem in actual society. Some falls result in injuries and the cost associated with their treatment is high. This is a complex problem that requires several steps in order to be tackled. Firstly, it is crucial to develop strat...

Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments.

Acta orthopaedica
Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identify...

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs.

European radiology
OBJECTIVE: To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs). Hip fracture is a leading worldwide health problem for the elde...

Validation of a Deep Learning Model to Screen for Glaucoma Using Images from Different Fundus Cameras and Data Augmentation.

Ophthalmology. Glaucoma
PURPOSE: To validate a deep residual learning algorithm to diagnose glaucoma from fundus photography using different fundus cameras at different institutes.

Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

European radiology
OBJECTIVES: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).

Computed Tomography-Based Radiomic Features Could Potentially Predict Microsatellite Instability Status in Stage II Colorectal Cancer: A Preliminary Study.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate whether quantitative radiomics features extracted from computed tomography (CT) can predict microsatellite instability (MSI) status in an Asian cohort of patients with stage Ⅱ colorectal cancer (CRC).