AIMC Topic: Middle Aged

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Multicenter validation of an artificial intelligence (AI)-based platform for the diagnosis of acute appendicitis.

Surgery
BACKGROUND: The current scores used to help diagnose acute appendicitis have a "gray" zone in which the diagnosis is usually inconclusive. Furthermore, the universal use of CT scanning is limited because of the radiation hazards and/or limited resour...

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients.

Biomedical engineering online
BACKGROUND: Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strateg...

Public perceptions of artificial intelligence in healthcare: ethical concerns and opportunities for patient-centered care.

BMC medical ethics
BACKGROUND: In an effort to improve the quality of medical care, the philosophy of patient-centered care has become integrated into almost every aspect of the medical community. Despite its widespread acceptance, among patients and practitioners, the...

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers.

Scientific reports
Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible ...

Smartphone application for artificial intelligence-based evaluation of stool state during bowel preparation before colonoscopy.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: Colonoscopy (CS) is an important screening method for the early detection and removal of precancerous lesions. The stool state during bowel preparation (BP) should be properly evaluated to perform CS with sufficient quality. This study ai...

Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer.

European urology focus
BACKGROUND: The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual's cancer risk...

Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer.

European radiology
OBJECTIVES: This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients.

Assessment of Deep Learning-Based Triage Application for Acute Ischemic Stroke on Brain MRI in the ER.

Academic radiology
RATIONALE AND OBJECTIVES: To assess a deep learning application (DLA) for acute ischemic stroke (AIS) detection on brain magnetic resonance imaging (MRI) in the emergency room (ER) and the effect of T2-weighted imaging (T2WI) on its performance.

Using machine learning to improve the diagnostic accuracy of the modified Duke/ESC 2015 criteria in patients with suspected prosthetic valve endocarditis - a proof of concept study.

European journal of nuclear medicine and molecular imaging
INTRODUCTION: Prosthetic valve endocarditis (PVE) is a serious complication of prosthetic valve implantation, with an estimated yearly incidence of at least 0.4-1.0%. The Duke criteria and subsequent modifications have been developed as a diagnostic ...

Spontaneous brain activity in patients with central retinal artery occlusion: a resting-state functional MRI study using machine learning.

Neuroreport
Central retinal artery occlusion (CRAO) is a serious eye condition that poses a risk to vision, resulting from the blockage of the central retinal artery. Because of the anatomical connection between the ocular artery, which derives from the internal...