AIMC Topic: Adult

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Benchmarking clinical risk prediction algorithms with ensemble machine learning for the noninvasive diagnosis of liver fibrosis in NAFLD.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Ensemble machine-learning methods, like the superlearner, combine multiple models into a single one to enhance predictive accuracy. Here we explore the potential of the superlearner as a benchmarking tool for clinical risk predic...

A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Obtaining automated, objective 3-dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies ...

Automated Measurement and Three-Dimensional Fitting of Corneal Ulcerations and Erosions via AI-Based Image Analysis.

Current eye research
PURPOSE: Artificial intelligence (AI)-tools hold great potential to compensate for missing resources in health-care systems but often fail to be implemented in clinical routine. Intriguingly, no-code and low-code technologies allow clinicians to deve...

Navigating merits and limits on the current perspectives and ethical challenges in the utilization of artificial intelligence in psychiatry - An exploratory mixed methods study.

Asian journal of psychiatry
BACKGROUND: The integration of Artificial Intelligence (AI) in psychiatry presents opportunities for enhancing patient care but raises significant ethical concerns and challenges in clinical application. Addressing these challenges necessitates an in...

Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients.

The journal of obstetrics and gynaecology research
AIM: To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecological malignant tumor patients, and to explore the value of machine learning (ML) models in VTE occurren...

The role of artificial intelligence in training ENT residents: a survey on ChatGPT, a new method of investigation.

Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale
OBJECTIVE: The primary focus of this study was to analyze the adoption of ChatGPT among Ear, Nose, and Throat (ENT) trainees, encompassing its role in scientific research and personal study. We examined in which year ENT trainees become involved in c...

Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.

Journal of neuro-oncology
OBJECTIVE: Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differe...

Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research.

Creative nursing
The integration of artificial intelligence (AI) into health care offers the potential to enhance patient care, improve diagnostic precision, and broaden access to health-care services. Nurses, positioned at the forefront of patient care, play a pivot...

Machine learning assisted 5-part tooth segmentation method for CBCT-based dental age estimation in adults.

The Journal of forensic odonto-stomatology
BACKGROUND: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG) method. Additionally, superv...

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

Journal of translational medicine
BACKGROUND: Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related ...