AIMC Topic: Female

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Genomic determinants of biological age estimated by deep learning applied to retinal images.

GeroScience
With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between c...

Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.

Clinical oncology (Royal College of Radiologists (Great Britain))
Artificial intelligence (AI) advancements have accelerated applications of imaging in clinical oncology, especially in revolutionizing the safe and accurate delivery of state-of-the-art imaging-guided radiotherapy techniques. However, concerns are gr...

Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study.

The American journal of clinical nutrition
BACKGROUND: Cachexia is associated with multiple adverse outcomes in cancer. However, clinical decision-making for oncology patients at the cachexia stage presents significant challenges.

AI-based models to predict decompensation on traumatic brain injury patients.

Computers in biology and medicine
Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%-40% in severe cases. This study ...

AIScholar: An OpenFaaS-enhanced cloud platform for intelligent medical data analytics.

Computers in biology and medicine
This paper presents AIScholar, an intelligent research cloud platform developed based on artificial intelligence analysis methods and the OpenFaaS serverless framework, designed for intelligent analysis of clinical medical data with high scalability....

CT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence Tools in Over 100,000 Patients.

AJR. American journal of roentgenology
CT-based abdominal body composition measures have shown associations with important health outcomes. Advances in artificial intelligence (AI) now allow deployment of tools that measure body composition in large patient populations. The purpose of t...

Machine Learning to Detect Cervical Spine Fractures Missed by Radiologists on CT: Analysis Using Seven Award-Winning Models From the 2022 RSNA Cervical Spine Fracture AI Challenge.

AJR. American journal of roentgenology
Available data on radiologists' missed cervical spine fractures are based primarily on studies using human reviewers to identify errors on reevaluation; such studies do not capture the full extent of missed fractures. The purpose of this study was ...

Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

Seizure
PURPOSE: Compare the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health records (EHR) using human review versus natural language processing (NLP) assisted review.

Deep learning model to identify and validate hypotension endotypes in surgical and critically ill patients.

British journal of anaesthesia
BACKGROUND: Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aime...

Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning.

Journal of Alzheimer's disease : JAD
BACKGROUND: Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effec...