AIMC Topic: Aged

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BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.

Journal of neuroradiology = Journal de neuroradiologie
INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving ther...

SPACE: Subregion Perfusion Analysis for Comprehensive Evaluation of Breast Tumor Using Contrast-Enhanced Ultrasound-A Retrospective and Prospective Multicenter Cohort Study.

Ultrasound in medicine & biology
OBJECTIVE: To develop a dynamic contrast-enhanced ultrasound (CEUS)-based method for segmenting tumor perfusion subregions, quantifying tumor heterogeneity, and constructing models for distinguishing benign from malignant breast tumors.

Development of a deep learning-based automated diagnostic system (DLADS) for classifying mammographic lesions - a first large-scale multi-institutional clinical trial in Japan.

Breast cancer (Tokyo, Japan)
BACKGROUND: Recently, western countries have built evidence on mammographic artificial Intelligence-computer-aided diagnosis (AI-CADx) systems; however, their effectiveness has not yet been sufficiently validated in Japanese women. In this study, we ...

Imaging-based Surgical Site Infection Detection Using Artificial Intelligence.

Annals of surgery
OBJECTIVE: To develop an artificial intelligence-based pipeline to assess and triage patient-submitted postoperative wound images.

Multi-modal models using fMRI, urine and serum biomarkers for classification and risk prognosis in diabetic kidney disease.

Diabetes, obesity & metabolism
BACKGROUND: Functional magnetic resonance imaging (fMRI) is a powerful tool for non-invasive evaluation of micro-changes in the kidneys. This study aims to develop classification and prognostic models based on multi-modal data.

From Faster Frames to Flawless Focus: Deep Learning HASTE in Postoperative Single Sequence MRI.

Academic radiology
BACKGROUND: This study evaluates the feasibility of a novel deep learning-accelerated half-fourier single-shot turbo spin-echo sequence (HASTE-DL) compared to the conventional HASTE sequence (HASTE) in postoperative single-sequence MRI for the detect...

MRI Radiomics and Automated Habitat Analysis Enhance Machine Learning Prediction of Bone Metastasis and High-Grade Gleason Scores in Prostate Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To explore the value of machine learning models based on MRI radiomics and automated habitat analysis in predicting bone metastasis and high-grade pathological Gleason scores in prostate cancer.

International Consensus Histopathological Criteria for Subtyping Idiopathic Multicentric Castleman Disease Based on Machine Learning Analysis.

American journal of hematology
Idiopathic multicentric Castleman disease (iMCD) is a rare lymphoproliferative disorder classified into three recognized clinical subtypes-idiopathic plasmacytic lymphadenopathy (IPL), TAFRO, and NOS. Although clinical criteria are available for subt...

Artificial Intelligence Language Models to Translate Professional Radiology Mammography Reports Into Plain Language - Impact on Interpretability and Perception by Patients.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to evaluate the interpretability and patient perception of AI-translated mammography and sonography reports, focusing on comprehensibility, follow-up recommendations, and conveyed empathy using a survey.