AIMC Topic: Aged, 80 and over

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Anatomical context improves deep learning on the brain age estimation task.

Magnetic resonance imaging
Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network desig...

MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

European journal of radiology
PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with ...

Artificial neural network models to predict nodal status in clinically node-negative breast cancer.

BMC cancer
BACKGROUND: Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical s...

Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model.

The Journal of arthroplasty
BACKGROUND: The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee arthroplasty (TKA). The secondary objective ...

Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies.

Scientific reports
The differentiation of dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) using brain perfusion single photon emission tomography is important but is challenging because these conditions exhibit typical features. The cingulate island sign ...

Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set.

Magnetic resonance imaging
Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance ...

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE).

IEEE transactions on medical imaging
In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enfor...

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance ...

Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks.

Journal of neuroscience methods
BACKGROUND: Human cortical primary sulci are relatively stable landmarks and commonly observed across the population. Despite their stability, the primary sulci exhibit phenotypic variability.