AIMC Topic: Male

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Who is responsible? US Public perceptions of AI governance through the lenses of trust and ethics.

Public understanding of science (Bristol, England)
The governance of artificial intelligence (AI) is an urgent challenge that requires actions from three interdependent stakeholders: individual citizens, technology corporations, and governments. We conducted an online survey ( = 525) of US adults to ...

Deep learning based detection of osteophytes in radiographs and magnetic resonance imagings of the knee using 2D and 3D morphology.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-r...

Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning.

American heart journal
BACKGROUND: Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk a...

Prospective Comparison of Free-Breathing Accelerated Cine Deep Learning Reconstruction Versus Standard Breath-Hold Cardiac MRI Sequences in Patients With Ischemic Heart Disease.

AJR. American journal of roentgenology
Cine cardiac MRI sequences require repeated breath-holds, which can be difficult for patients with ischemic heart disease (IHD). The purpose of the study was to compare a free-breathing accelerated cine sequence using deep learning (DL) reconstruct...

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

Molecular psychiatry
White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the gener...

Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval.

Orthodontics & craniofacial research
OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs).

Predicting T-Cell Lymphoma in Children From F-FDG PET-CT Imaging With Multiple Machine Learning Models.

Journal of imaging informatics in medicine
This study aimed to examine the feasibility of utilizing radiomics models derived from F-FDG PET/CT imaging to screen for T-cell lymphoma in children with lymphoma. All patients had undergone F-FDG PET/CT scans. Lesions were extracted from PET/CT and...

[Not Available].

Medical physics
BACKGROUND:: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in r...

Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis.

Medicina (Kaunas, Lithuania)
: Infertility rates and the number of couples undergoing reproductive care have both increased substantially during the last few decades. Semen analysis is a crucial step in both the diagnosis and the treatment of male infertility. The accuracy of se...

Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators.

Practical radiation oncology
PURPOSE: The purpose of this investigation was to evaluate the clinical applicability of a commercial artificial intelligence-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone beam computed tomography (iCBCT) acquisitions ...