AIMC Topic: Female

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Vertebral compression fractures at abdominal CT: underdiagnosis, undertreatment, and evaluation of an AI algorithm.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Vertebral compression fractures (VCFs) are common and indicate a high future risk of additional osteoporotic fractures. However, many VCFs are unreported by radiologists, and even if reported, many patients do not receive treatment. The purpose of th...

Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK Biobank.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Fracture prediction is essential in managing patients with osteoporosis and is an integral component of many fracture prevention guidelines. We aimed to identify the most relevant clinical fracture risk factors in contemporary populations by training...

Machine learning-based analysis identifies and validates serum exosomal proteomic signatures for the diagnosis of colorectal cancer.

Cell reports. Medicine
The potential of serum extracellular vesicles (EVs) as non-invasive biomarkers for diagnosing colorectal cancer (CRC) remains elusive. We employed an in-depth 4D-DIA proteomics and machine learning (ML) pipeline to identify key proteins, PF4 and AACT...

Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-Based Machine Learning Approach.

Military medicine
INTRODUCTION: Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percen...

Assessment of readability, reliability, and quality of ChatGPT®, BARD®, Gemini®, Copilot®, Perplexity® responses on palliative care.

Medicine
There is no study that comprehensively evaluates data on the readability and quality of "palliative care" information provided by artificial intelligence (AI) chatbots ChatGPT®, Bard®, Gemini®, Copilot®, Perplexity®. Our study is an observational and...

Machine learning models for differential diagnosing HER2-low breast cancer: A radiomics approach.

Medicine
To develop machine learning models based on preoperative dynamic enhanced magnetic resonance imaging (DCE-MRI) radiomics and to explore their potential prognostic value in the differential diagnosis of human epidermal growth factor receptor 2 (HER2)-...

CT radiomics-based machine learning model for differentiating between enchondroma and low-grade chondrosarcoma.

Medicine
It may be difficult to distinguish between enchondroma and low-grade malignant cartilage tumors (grade 1) radiologically. This study aimed to construct machine learning models using 3D computed tomography (CT)-based radiomics analysis to differentiat...

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.

Human brain mapping
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts,...

Deep Learning-based Segmentation of Computed Tomography Scans Predicts Disease Progression and Mortality in Idiopathic Pulmonary Fibrosis.

American journal of respiratory and critical care medicine
Despite evidence demonstrating a prognostic role for computed tomography (CT) scans in idiopathic pulmonary fibrosis (IPF), image-based biomarkers are not routinely used in clinical practice or trials. To develop automated imaging biomarkers using ...