AI Medical Compendium Topic:
Cross-Sectional Studies

Clear Filters Showing 661 to 670 of 1223 articles

Automated Whole-Liver MRI Segmentation to Assess Steatosis and Iron Quantification in Chronic Liver Disease.

Radiology
Background Standardized manual region of interest (ROI) sampling strategies for hepatic MRI steatosis and iron quantification are time consuming, with variable results. Purpose To evaluate the performance of automatic MRI whole-liver segmentation (WL...

Accurate Identification of the Trabecular Meshwork under Gonioscopic View in Real Time Using Deep Learning.

Ophthalmology. Glaucoma
PURPOSE: Accurate identification of iridocorneal structures on gonioscopy is difficult to master, and errors can lead to grave surgical complications. This study aimed to develop and train convolutional neural networks (CNNs) to accurately identify t...

Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study.

Skeletal radiology
OBJECTIVES: Accurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance...

A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition.

Nutrients
BACKGROUND: Sarcopenic dysphagia, a swallowing disorder caused by sarcopenia, is prevalent in older patients and can cause malnutrition and aspiration pneumonia. This study aimed to develop a simple screening test using image recognition with a low r...

Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study.

Frontiers in public health
What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks? Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decade...

Building a predictive model to assist in the diagnosis of cervical cancer.

Future oncology (London, England)
Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Thi...

Natural Language Processing and Machine Learning Methods to Characterize Unstructured Patient-Reported Outcomes: Validation Study.

Journal of medical Internet research
BACKGROUND: Assessing patient-reported outcomes (PROs) through interviews or conversations during clinical encounters provides insightful information about survivorship.

The Impact of Explanations on Layperson Trust in Artificial Intelligence-Driven Symptom Checker Apps: Experimental Study.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI)-driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must tr...

Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy.

JAMA network open
IMPORTANCE: Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Early detection and intervention can prevent blindness; however, many patients do not receive their recommended annual diabetic eye examinations, primarily owi...

Association of Individual and Community Factors With Hepatitis C Infections Among Pregnant People and Newborns.

JAMA health forum
IMPORTANCE: The opioid crisis has increasingly affected pregnant people and infants. Hepatitis C virus (HCV) infections, a known complication of opioid use, grew in parallel with opioid-related complications; however, the literature informing individ...