AIMC Topic: Male

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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 ...

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...

Estimation of postmortem interval under different ambient temperatures based on multi-organ metabolomics and machine learning algorithm.

International journal of legal medicine
In forensic practice, the estimation of postmortem interval has been a persistent challenge. Recently, there has been an increasing utilization of metabolomics techniques combined with machine learning methods for postmortem interval estimation. When...

A Learning Paradigm for Selecting Few Discriminative Stimuli in Eye-Tracking Research.

IEEE transactions on pattern analysis and machine intelligence
Eye-tracking is a reliable method for quantifying visual information processing and holds significant potential for group recognition, such as identifying autism spectrum disorder (ASD). However, eye-tracking research typically faces the heterogeneit...

Automated detection of severe cerebral edema using explainable deep transfer learning after hypoxic ischemic brain injury.

Resuscitation
BACKGROUND: Substantial gaps exist in the neuroprognostication of cardiac arrest patients who remain comatose after the restoration of spontaneous circulation. Most studies focus on predicting survival, a measure confounded by the withdrawal of life-...

Deep Learning and Radiomic Signatures Associated with Tumor Immune Heterogeneity Predict Microvascular Invasion in Colon Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: This study aims to develop and validate a deep learning radiomics signature (DLRS) that integrates radiomics and deep learning features for the non-invasive prediction of microvascular invasion (MVI) in patients with colon c...