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Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Nov 7, 2024
INTRODUCTION: Alzheimer's disease (AD) is the most common form of dementia in the elderly. Given that AD neuropathology begins decades before symptoms, there is a dire need for effective screening tools for early detection of AD to facilitate early i...
Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.
Scientific reports
Nov 7, 2024
Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest predictive value. Machine learning (ML) may achieve greater results when predicting adverse outcomes in patients with recently diagnosed AF. Several ML m...
Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source optical coherence tomography-angiography.
Scientific reports
Nov 7, 2024
The recent rise of artificial intelligence represents a revolutionary way of improving current medical practices, including cardiovascular (CV) assessment scores. Retinal vascular alterations may reflect systemic processes such as the presence of CV ...
Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence.
Scientific reports
Nov 7, 2024
This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-instrument training dataset composed of Optical coherence tomography-angiography (OCTA) images from tw...
PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans.
Nature communications
Nov 7, 2024
Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abd...
An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer.
Nature communications
Nov 7, 2024
Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consider...
Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial.
AJNR. American journal of neuroradiology
Nov 7, 2024
BACKGROUND AND PURPOSE: Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However, the use of artificial intelligence by trainees across different levels of experience has not been ...
Deep Learning-Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study.
AJNR. American journal of neuroradiology
Nov 7, 2024
BACKGROUND AND PURPOSE: Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. He...
Utilizing artificial intelligence to determine bone mineral density using spectral CT.
Bone
Nov 6, 2024
BACKGROUND: Dual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-water to respond to BMD changes without requiring dedicated software or calibration. Artificial intelligence (AI) has been utilized for diagnosising oste...
Image quality in three-dimensional (3D) contrast-enhanced dynamic magnetic resonance imaging of the abdomen using deep learning denoising technique: intraindividual comparison between T1-weighted sequences with compressed sensing and with a modified Fast 3D mode wheel.
Japanese journal of radiology
Nov 6, 2024
PURPOSE: To assess the image quality of a modified Fast three-dimensional (Fast 3D) mode wheel with sequential data filling (mFast 3D wheel) combined with a deep learning denoising technique (Advanced Intelligent Clear-IQ Engine [AiCE]) in contrast-e...
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