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Learning CT-free attenuation-corrected total-body PET images through deep learning.
European radiology
Feb 15, 2024
OBJECTIVES: Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radi...
Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study.
Journal of neurotrauma
Feb 15, 2024
Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imagin...
Clinical evaluation of Artificial Intelligence Driven Remote Monitoring technology for assessment of patient oral hygiene during orthodontic treatment.
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Feb 15, 2024
INTRODUCTION: This study aimed to clinically evaluate the accuracy of Dental Monitoring's (DM) artificial intelligence (AI) image analysis and oral hygiene notification algorithm in identifying oral hygiene and mucogingival conditions.
Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan.
Fluids and barriers of the CNS
Feb 13, 2024
BACKGROUND: Peri-sinus structures such as arachnoid granulations (AG) and the parasagittal dural (PSD) space have gained much recent attention as sites of cerebral spinal fluid (CSF) egress and neuroimmune surveillance. Neurofluid circulation dysfunc...
Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.
Acta radiologica (Stockholm, Sweden : 1987)
Feb 11, 2024
BACKGROUND: The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition.
Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height.
European radiology
Feb 10, 2024
OBJECTIVES: To develop and validate a deep learning-based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans.
Inconsistency between Human Observation and Deep Learning Models: Assessing Validity of Postmortem Computed Tomography Diagnosis of Drowning.
Journal of imaging informatics in medicine
Feb 9, 2024
Drowning diagnosis is a complicated process in the autopsy, even with the assistance of autopsy imaging and the on-site information from where the body was found. Previous studies have developed well-performed deep learning (DL) models for drowning d...
Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.
Molecular psychiatry
Feb 9, 2024
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psy...
Deep learning model to differentiate Crohn's disease from intestinal tuberculosis using histopathological whole slide images from intestinal specimens.
Virchows Archiv : an international journal of pathology
Feb 8, 2024
Crohn's disease (CD) and intestinal tuberculosis (ITB) share similar histopathological characteristics, and differential diagnosis can be a dilemma for pathologists. This study aimed to apply deep learning (DL) to analyze whole slide images (WSI) of ...
Using deep neural networks to disentangle visual and semantic information in human perception and memory.
Nature human behaviour
Feb 8, 2024
Mental representations of familiar categories are composed of visual and semantic information. Disentangling the contributions of visual and semantic information in humans is challenging because they are intermixed in mental representations. Deep neu...
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