AIMC Topic: Aged

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Automated Interstitial Lung Abnormality Probability Prediction at CT: A Stepwise Machine Learning Approach in the Boston Lung Cancer Study.

Radiology
Background It is increasingly recognized that interstitial lung abnormalities (ILAs) detected at CT have potential clinical implications, but automated identification of ILAs has not yet been fully established. Purpose To develop and test automated I...

Common and unique brain aging patterns between females and males quantified by large-scale deep learning.

Human brain mapping
There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently betwee...

Explainable machine learning prediction of edema adverse events in patients treated with tepotinib.

Clinical and translational science
Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there...

Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden S...

Development and Multicenter, Multiprotocol Validation of Neural Network for Aberrant Right Subclavian Artery Detection.

Yonsei medical journal
PURPOSE: This study aimed to develop and validate a convolutional neural network (CNN) that automatically detects an aberrant right subclavian artery (ARSA) on preoperative computed tomography (CT) for thyroid cancer evaluation.

Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

The journal of gene medicine
OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for ...

A tailored machine learning approach for mortality prediction in severe COVID-19 treated with glucocorticoids.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
BACKGROUNDThe impact of severe COVID-19 pneumonia on healthcare systems highlighted the need for accurate predictions to improve patient outcomes. Despite the established efficacy of glucocorticoids (GCs), variable patient respons...

ARTIFICIAL INTELLIGENCE-ENHANCED ANALYSIS OF RETINAL VASCULATURE IN AGE-RELATED MACULAR DEGENERATION.

Retina (Philadelphia, Pa.)
PURPOSE: To investigate associations between quantitative vascular measurements derived from intravenous fluorescein angiography (IVFA) and baseline characteristics on optical coherence tomography (OCT) in neovascular age-related macular degeneration...

Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets.

Radiology. Artificial intelligence
Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materi...