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Evaluation of minor labial salivary gland focus score in Sjögren's disease using deep learning: a tool for more efficient diagnosis and future tissue biomarker discovery.

Journal of autoimmunity
BACKGROUND: Sjögren's Disease (SjD) is histopathologically characterized by focal sialadenitis in minor labial salivary gland biopsies (mLSGB), which is evaluated by utilizing the focus score (FS). Focus score ≥1 identification is a critical step of ...

Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas.

NMR in biomedicine
Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiomas, SFTs exhibit a myoinositol peak on magnetic resonance spectroscopy (MRS). This study aimed to de...

Evaluation of AI Summaries on Interdisciplinary Understanding of Ophthalmology Notes.

JAMA ophthalmology
IMPORTANCE: Specialized ophthalmology terminology limits comprehension for nonophthalmology clinicians and professionals, hindering interdisciplinary communication and patient care. The clinical implementation of large language models (LLMs) into pra...

Predicting Respiratory Disease Mortality Risk Using Open-Source AI on Chest Radiographs in an Asian Health Screening Population.

Radiology. Artificial intelligence
Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs algorithm, CXR-Lung-Risk, for stratifying respiratory disease mortality risk among an Asian health screening population using baseline and follow-up chest ...

Unsupervised Deep Learning for Blood-Brain Barrier Leakage Detection in Diffuse Glioma Using Dynamic Contrast-enhanced MRI.

Radiology. Artificial intelligence
Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier leakage detection using dynamic contrast-enhanced MRI, without requiring pharmacokinetic models and arterial input function estimation. Materials and Met...

Evaluating Performance of a Deep Learning Multilabel Segmentation Model to Quantify Acute and Chronic Brain Lesions at MRI after Stroke and Predict Prognosis.

Radiology. Artificial intelligence
Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after acute ischemic stroke (AIS) and assess relationships between clinical and model-extracted radiologi...

The robotic companion's choice encourages sustainable food choices: The moderating role of anthropomorphism.

Applied psychology. Health and well-being
Previous research has shown the impact of the food choices of others on individuals' own food choices. We conducted two studies to investigate how a robot's choice might influence people's choices between meat-heavy and vegetable-forward meals in ima...

Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) ...

Development and Validation of a Sham-AI Model for Intracranial Aneurysm Detection at CT Angiography.

Radiology. Artificial intelligence
Purpose To evaluate a sham-artificial intelligence (AI) model acting as a placebo control for a standard-AI model for diagnosis of intracranial aneurysm. Materials and Methods This retrospective crossover, blinded, multireader, multicase study was co...

Automated Imaging Differentiation for Parkinsonism.

JAMA neurology
IMPORTANCE: Magnetic resonance imaging (MRI) paired with appropriate disease-specific machine learning holds promise for the clinical differentiation of Parkinson disease (PD), multiple system atrophy (MSA) parkinsonian variant, and progressive supra...