AIMC Topic: Adult

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Effect of artificial intelligence driven therapeutic lifestyle changes (AI-TLC) intervention on health behavior and health among obesity pregnant women in China: a randomized controlled trial protocol.

Frontiers in public health
INTRODUCTION: Obesity has reached epidemic proportions globally, posing significant challenges to public health and economic stability. In China, the prevalence of obesity is increasing rapidly, particularly among pregnant women, who face unique risk...

Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.

Frontiers in endocrinology
OBJECTIVE: To develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).

Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model.

PeerJ
BACKGROUND: Tuberculous pleural effusion (TPE) is a prevalent tuberculosis complication, with diagnosis presenting considerable challenges. Timely and precise identification of TPE is vital for effective patient management and prognosis, yet existing...

Shaping the future of medical education: A cross-sectional study on ChatGPT attitude and usage among medical students in Sudan.

PloS one
BACKGROUND: Artificial intelligence (AI) is revolutionizing education globally, yet its adoption in medical education remains inadequately understood. ChatGPT, a generative AI tool, offers promising yet doubtful potential for enhancing academic and c...

Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis.

PloS one
INTRODUCTION: Tuberculous meningitis (TBM) leads to high mortality, especially amongst individuals with HIV. Predicting the incidence of disease-related complications is challenging, for which purpose the value of brain magnetic resonance imaging (MR...

Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.

Depression and anxiety
Clinically significant anxiety (CSA) is common in individuals with short-term insomnia. This study aims to explore the relationship between CSA and the subjective and objective parameters of sleep in patients with short-term insomnia and construct ma...

Deep learning approaches for quantitative and qualitative assessment of cervical vertebral maturation staging systems.

PloS one
To investigate the potential of artificial intelligence (AI) in Cervical Vertebral Maturation (CVM) staging, we developed and compared AI-based qualitative CVM and AI-based quantitative QCVM methods. A dataset of 3,600 lateral cephalometric images fr...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

PloS one
Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weight gain is augmenting the disease burden and healthcare expenditure. However, predictors of psychotro...

Developing a smart system for binary classification of disordered voices using machine learning.

American journal of otolaryngology
OBJECTIVES: Voice disorder is characterized by disruptions in voice quality caused by issues in vocal fold vibration during phonation. The study explored the application of machine learning, based on the Random Forest (RF) and Decision Tree (DT) mode...

Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.

PloS one
Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors ca...