AIMC Topic: Deep Learning

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Utilising routinely collected clinical data through time series deep learning to improve identification of bacterial bloodstream infections: a retrospective cohort study.

The Lancet. Digital health
BACKGROUND: Blood cultures are the gold standard for diagnosing bacterial bloodstream infections, but test results are only available 24-48 h after sampling. We aimed to develop and evaluate models using health-care data to predict bloodstream infect...

MRI-Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND & AIMS: Microvascular invasion (MVI) is associated with poor prognosis in hepatocellular carcinoma (HCC). Topology may improve the predictive performance and interpretability of deep learning (DL). We aimed to develop and externally valida...

Deep Learning-based Brain Age Prediction Using MRI to Identify Fetuses with Cerebral Ventriculomegaly.

Radiology. Artificial intelligence
Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a two-dim...

A Veterinary DICOM-Based Deep Learning Denoising Algorithm Can Improve Subjective and Objective Brain MRI Image Quality.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
In this analytical cross-sectional method comparison study, we evaluated brain MR images in 30 dogs and cats with and without using a DICOM-based deep-learning (DL) denoising algorithm developed specifically for veterinary patients. Quantitative comp...

AtSubP-2.0: An integrated web server for the annotation of Arabidopsis proteome subcellular localization using deep learning.

The plant genome
The organization of subcellular components in a cell is critical for its function and studying cellular processes, protein-protein interactions, identifying potential drug targets, network analysis, and other systems biology mechanisms. Determining p...

Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI.

Radiology. Artificial intelligence
Purpose To evaluate the performance of Physics-Informed Autoencoder (PIA), a self-supervised deep learning model, in measuring tissue-based biomarkers for prostate cancer (PCa) using hybrid multidimensional MRI. Materials and Methods This retrospecti...

Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data.

NMR in biomedicine
The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3 and 0.55 T. A total of 35 healthy volunteers underwent conventional twofold accelerated MRCP scans at field...

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

JAMA cardiology
IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of arti...

NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.

Radiology. Artificial intelligence
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computation...

From statistics to deep learning: Using large language models in psychiatric research.

International journal of methods in psychiatric research
BACKGROUND: Large Language Models (LLMs) hold promise in enhancing psychiatric research efficiency. However, concerns related to bias, computational demands, data privacy, and the reliability of LLM-generated content pose challenges. GAP: Existing st...