AIMC Topic: Middle Aged

Clear Filters Showing 2991 to 3000 of 17155 articles

Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks.

Scientific reports
Parkinson's disease is a neurodegenerative disorder that is associated with aging, leading to the progressive deterioration of certain regions of the brain. Accurate and timely diagnosis plays a crucial role in facilitating optimal therapy and improv...

Metabolomics and machine learning approaches for diagnostic biomarkers screening in systemic light chain amyloidosis.

Annals of hematology
Delayed diagnosis of systemic light chain (AL) amyloidosis is common and associated with worse survival and early mortality. Current diagnosis still relies on invasive tissue biopsies, highlighting the need for sensitive, noninvasive biomarkers for e...

Compliance Evaluation with ChatGPT for Diagnosis and Treatment in Patients Brought to the ED with a Preliminary Diagnosis of Stroke.

Prehospital emergency care
OBJECTIVES: Chat Generative Pre-trained Transformer (ChatGPT) is a natural language processing product developed by OpenAI. Recently, the use of ChatGPT has gained attention in the field of health care, particularly for its potential applications in ...

Association between muscle mass assessed by an artificial intelligence-based ultrasound imaging system and quality of life in patients with cancer-related malnutrition.

Nutrition (Burbank, Los Angeles County, Calif.)
INTRODUCTION: Emerging evidence suggests that diminished skeletal muscle mass is associated with lower health-related quality of life (HRQOL) in individuals with cancer. There are no studies that we know of in the literature that use ultrasound syste...

Automated Fast Prediction of Bone Mineral Density From Low-dose Computed Tomography.

Academic radiology
BACKGROUND: Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessment of volumetric bone mineral density (vBMD) and the diagnosis of osteoporosis (OP).

Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement.

Japanese journal of radiology
PURPOSE: Myocardial computed tomography (CT) late enhancement (LE) allows assessment of myocardial scarring. Super-resolution deep learning image reconstruction (SR-DLR) trained on data acquired from ultra-high-resolution CT may improve image quality...

AI-based personalized real-time risk prediction for behavioral management in psychiatric wards using multimodal data.

International journal of medical informatics
BACKGROUND: Suicide is a major global health issue, with approximately 700,000 deaths annually (WHO). In psychiatric wards, managing harmful behaviors such as suicide, self-harm, and aggression is essential to ensure patient and staff safety. However...

Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial.

World neurosurgery
BACKGROUND: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnostic workloads place significant strain on radiologists. We evaluated the efficacy of a deep learning a...

Artificial intelligence (ChatGPT 4.0) vs. Human expertise for epileptic seizure and epilepsy diagnosis and classification in Adults: An exploratory study.

Epilepsy & behavior : E&B
AIMS: Artificial intelligence (AI) tools like ChatGPT hold promise for enhancing diagnostic accuracy and efficiency in clinical practice. This exploratory study evaluates ChatGPT's performance in diagnosing and classifying epileptic seizures, epileps...

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine l...