AIMC Topic: Middle Aged

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The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study.

The Lancet. Digital health
BACKGROUND: Artificial intelligence (AI) applications in health care have been effective in many areas of medicine, but they are often trained for a single task using labelled data, making deployment and generalisability challenging. How well a gener...

Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assi...

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence...

A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.

The British journal of radiology
OBJECTIVES: To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image quality in patients with stent-assisted coil embolization, coil embolization, and flow-diverting stent ...

Can we screen opportunistically for low bone mineral density using CT scans of the shoulder and artificial intelligence?

The British journal of radiology
OBJECTIVE: To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopenia/osteoporosis), and to compare the performance of two machine learning models to predict low BMD.

Interventional cardiologists' perspectives and knowledge towards artificial intelligence.

The Journal of invasive cardiology
BACKGROUND: Artificial intelligence (AI) is increasingly utilized in interventional cardiology (IC) and holds the potential to revolutionize the field.

Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likeliho...

Predicting intraocular lens tilt using a machine learning concept.

Journal of cataract and refractive surgery
PURPOSE: To use a combination of partial least squares regression and a machine learning approach to predict intraocular lens (IOL) tilt using preoperative biometry data.

A Prospective Analysis of Screen-Detected Cancers Recalled and Not Recalled by Artificial Intelligence.

Journal of breast imaging
OBJECTIVE: The use of artificial intelligence has potential in assisting many aspects of imaging interpretation. We undertook a prospective service evaluation from March to October 2022 of Mammography Intelligent Assessment (MIA) operating "silently"...

Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.

Schizophrenia bulletin
BACKGROUND AND HYPOTHESIS: The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), an...