AIMC Topic: Female

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Artificial Intelligence in Breast Imaging: Potentials and Limitations.

AJR. American journal of roentgenology
OBJECTIVE: The purpose of this article is to discuss potential applications of artificial intelligence (AI) in breast imaging and limitations that may slow or prevent its adoption.

A Review of the Role of Augmented Intelligence in Breast Imaging: From Automated Breast Density Assessment to Risk Stratification.

AJR. American journal of roentgenology
OBJECTIVE: The goal of augmented intelligence is to increase efficiency and effectiveness in practice. To achieve this, augmented intelligence technologies are being asked to perform a range of tasks, from simple to complex and quantitative. The deve...

Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs.

Radiology
Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-performance automated binary classification of chest radiographs. Materials and Methods In a retrospective study, 216 431 frontal chest radiographs obtained between ...

Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI.

Radiology
Purpose To compare the diagnostic performance of radiomic analysis (RA) and a convolutional neural network (CNN) to radiologists for classification of contrast agent-enhancing lesions as benign or malignant at multiparametric breast MRI. Materials an...

Arterial stiffness in normal pregnancy as assessed by digital pulse wave analysis by photoplethysmography - A longitudinal study.

Pregnancy hypertension
INTRODUCTION: It might in the future be valuable to screen for increased maternal arterial stiffness, i.e. low compliance, since it is associated with development of hypertensive complications in pregnancy. Digital pulse wave analysis (DPA) is an eas...

Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We propose to discriminate the pathological grades directly on digital mammograms instead of pathological images. An end-to-end learning algorithm based on the combined multi-level features is proposed. Low-level features are extracted and selected b...

Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort.

PLoS medicine
BACKGROUND: The relationship between allergic sensitisation and asthma is complex; the data about the strength of this association are conflicting. We propose that the discrepancies arise in part because allergic sensitisation may not be a single ent...