AIMC Topic: Female

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A Mixed-attention Network for Automated Interventricular Septum Segmentation in Bright-blood Myocardial T2* MRI Relaxometry in Thalassemia.

Academic radiology
RATIONALE AND OBJECTIVES: This study develops a deep-learning method for automatic segmentation of the interventricular septum (IS) in MR images to measure myocardial T2* and estimate cardiac iron deposition in patients with thalassemia.

A Study on Predicting the Efficacy of Posterior Lumbar Interbody Fusion Surgery Using a Deep Learning Radiomics Model.

Academic radiology
RATIONALE AND OBJECTIVES: This study seeks to develop a combined model integrating clinical data, radiomics, and deep learning (DL) for predicting the efficacy of posterior lumbar interbody fusion (PLIF) surgery.

Deep Learning CAIPIRINHA-VIBE Improves and Accelerates Head and Neck MRI.

Academic radiology
RATIONALE AND OBJECTIVES: The aim of this study was to evaluate image quality for contrast-enhanced (CE) neck MRI with a deep learning-reconstructed VIBE sequence with acceleration factors (AF) 4 (DL4-VIBE) and 6 (DL6-VIBE).

Deep Learning Differentiates Papilledema, NAION, and Healthy Eyes With Unsegmented 3D OCT Volumes.

American journal of ophthalmology
OBJECTIVE: Deep learning (DL) has been used to differentiate papilledema from healthy eyes and optic disc elevation on fundus photos. As we described optic nerve head (ONH) and peripapillary retina (PPR) optical coherence tomography (OCT) features th...

Deep learning model for malignancy prediction of TI-RADS 4 thyroid nodules with high-risk characteristics using multimodal ultrasound: A multicentre study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The automatic screening of thyroid nodules using computer-aided diagnosis holds great promise in reducing missed and misdiagnosed cases in clinical practice. However, most current research focuses on single-modal images and does not fully leverage th...

Interpretable Machine Learning Models for Differentiating Glioblastoma From Solitary Brain Metastasis Using Radiomics.

Academic radiology
PURPOSE: To develop and validate interpretable machine learning models for differentiating glioblastoma (GB) from solitary brain metastasis (SBM) using radiomics features from contrast-enhanced T1-weighted MRI (CE-T1WI), and to compare the impact of ...

Improving Breast Cancer Diagnosis in Ultrasound Images Using Deep Learning with Feature Fusion and Attention Mechanism.

Academic radiology
RATIONALE AND OBJECTIVES: Early detection of malignant lesions in ultrasound images is crucial for effective cancer diagnosis and treatment. While traditional methods rely on radiologists, deep learning models can improve accuracy, reduce errors, and...

SFPGCL: Specificity-preserving federated population graph contrastive learning for multi-site ASD identification using rs-fMRI data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects people's social communication and daily routine. Most existing imaging studies on ASD use single site resting-state functional magnetic resonance imaging (rs-fMRI) da...

Heterogeneous associations between early-life religious upbringing and late-life health: Evidence from a machine learning approach.

Social science & medicine (1982)
Religious upbringing was common in Europe during the childhood of older adults today. However, studies are still lacking on how early-life religious upbringing is associated with adult health and how this association differs in different population s...