AIMC Topic: Adult

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[Serum proteomics and machine learning unveil new diagnostic biomarkers for tuberculosis in adolescents and young adults].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Adolescents and young adults (AYAs) are one of the major populations susceptible to tuberculosis. However, little is known about the unique characteristics and diagnostic biomarkers of tuberculosis in this population. In this study, 81 AYAs were recr...

Detection of precancerous lesions in cervical images of perimenopausal women using U-net deep learning.

African journal of reproductive health
Due to physiological changes during the perimenopausal period, the morphology of cervical cells undergoes certain alterations. Accurate cell image segmentation and lesion identification are of great significance for the early detection of precancerou...

Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study.

Radiation protection dosimetry
We created and validated an open-access AI algorithm (AIc) for assessing image segmentation and patient centering in a multi-body-region, multi-center, and multi-scanner study. Our study included 825 head, chest, and abdomen-pelvis CT from 275 patien...

Machine Learning-Based Prediction of Large-for-Gestational-Age Infants in Mothers With Gestational Diabetes Mellitus.

The Journal of clinical endocrinology and metabolism
CONTEXT: Large-for-gestational-age (LGA), one of the most common complications of gestational diabetes mellitus (GDM), has become a global concern. The predictive performance of common continuous glucose monitoring (CGM) metrics for LGA is limited.

[Personalized mandibular reconstruction assisted by three-dimensional retrieval model based on fully connected neural network and a database of mandibles].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
OBJECTIVE: To propose a new protocol for personalized mandibular reconstruction assisted by three-dimensional (3D) retrieval model based on fully connected neural network (FCNN) and a database of mandibles, and to verify clinical feasibility of the p...

Utilizing Artificial Intelligence for Predicting Postoperative Complications in Breast Reduction Surgery: A Comprehensive Retrospective Analysis of Predictive Features and Outcomes.

Aesthetic surgery journal
BACKGROUND: Breast reduction is a common procedure with growing rates in the United States of America, aimed at alleviating the physical and psychological burdens of macromastia. Despite high success rates, it carries a risk of complications, with in...

Predicting Body Fat Percentage from Simple Anthropometric Measurements: A Machine Learning Approach.

Studies in health technology and informatics
Accurately assessing body fat percentage (BF%) is crucial for healthcare and fitness but is hindered by gold-standard methods that are costly and invasive. This study employs a dataset containing variables such as age, sex, Body Mass Index (BMI), and...

Assessment of Driver Inattention State Using Multimodal Wearable Signals and Cross-Attention-Driven Hierarchical Fusion.

Studies in health technology and informatics
Identifying driver inattention is crucial for road safety, driver well-being and can be enhanced using multimodal physiological signals. However, effective fusion of multimodal data is highly challenging, particularly with intermediate fusion, where ...

Knowledge and Perception of Artificial Intelligence in Medicine Among Undergraduate Medical Students in Sri Lanka: A Cross Sectional Study.

Studies in health technology and informatics
As AI is increasingly being used in medical practise, it is important to equip medical students with the concepts and principles. A cross-sectional study was conducted to understand medical students' knowledge and perception regarding the role of AI ...

Using C4.5 Algorithm to Gain Insights on Stakeholder Engagement and Use of Artificial Intelligence on Social Media in Dementia Caregiving Disparity Research.

Studies in health technology and informatics
We applied machine learning techniques to build models that predict perceived risks and benefits of using artificial intelligence (AI) algorithms to recruit African American informal caregivers for clinical trials and general health disparity researc...