AIMC Topic: Cross-Sectional Studies

Clear Filters Showing 1411 to 1420 of 1591 articles

Influence of vitamin D and calcium-sensing receptor gene variants on calcium metabolism in end-stage renal disease: insights from machine learning analysis.

European review for medical and pharmacological sciences
OBJECTIVE: End-stage renal disease (ESRD) commonly manifests with disrupted calcium balance, leading to renal osteodystrophy. We posited that variations in the genetic makeup of vitamin D and calcium-sensing receptors, specifically single nucleotide ...

Physician Opinions on Artificial Intelligence Chatbots In Dermatology: A National Online Cross-Sectional Survey of Dermatologists.

Journal of drugs in dermatology : JDD
BACKGROUND: Artificial intelligence chatbots (AIC) have sharply risen in popularity. Dermatology, heavily involving visual, clinical, and pathological pattern-recognition techniques, will be impacted by AIC. Thus, this study aims to categorize the at...

Machine learning prediction of early recurrence after surgery for gallbladder cancer.

The British journal of surgery
BACKGROUND: Gallbladder cancer is often associated with poor prognosis, especially when patients experience early recurrence after surgery. Machine learning may improve prediction accuracy by analysing complex non-linear relationships. The aim of thi...

A Meta-Learning Approach for Classifying Multimodal Retinal Images of Retinal Vein Occlusion With Limited Data.

Translational vision science & technology
PURPOSE: To propose and validate a meta-learning approach for detecting retinal vein occlusion (RVO) from multimodal images with only a few samples.

Artificial intelligence classifies primary progressive aphasia from connected speech.

Brain : a journal of neurology
Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants an...

Assessment of readability, reliability, and quality of ChatGPT®, BARD®, Gemini®, Copilot®, Perplexity® responses on palliative care.

Medicine
There is no study that comprehensively evaluates data on the readability and quality of "palliative care" information provided by artificial intelligence (AI) chatbots ChatGPT®, Bard®, Gemini®, Copilot®, Perplexity®. Our study is an observational and...

Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.

Translational vision science & technology
PURPOSE: To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data.

Artificial Intelligence Readiness, Perceptions, and Educational Needs Among Dental Students: A Cross-Sectional Study.

Clinical and experimental dental research
OBJECTIVES: With Artificial Intelligence (AI) profoundly affecting education, ensuring that students in health disciplines are ready to embrace AI is essential for their future workforce integration. This study aims to explore dental students' readin...

[Constructing a cataplexy face prediction model for narcolepsy type 1 based on ResNet-18].

Zhonghua yi xue za zhi
To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image recognition network ResNet-18. A cross-sectional study. Twenty-five narcolepsy type 1 patients who wer...