AIMC Topic: Cross-Sectional Studies
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External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.
International journal of cardiology
Jan 2, 2021
OBJECTIVE: To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population.
Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases.
Social work in public health
Dec 28, 2020
In the fight against Covid-19, developed countries and developing countries diverge in success. This drew attention to the discussion of how different health systems and different levels of health spending are effective in combating Covid-19. In this...
Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images.
The British journal of ophthalmology
Dec 21, 2020
BACKGROUND/AIMS: To apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.
Comparison of postural control in older adults under different dual-task conditions: A cross-sectional study.
Journal of bodywork and movement therapies
Dec 19, 2020
BACKGROUND: and purpose: Performing a cognitive task while maintaining postural stability, known as "dual-task" condition, can increase the cognitive demand and reduce the postural control capacity. The inability to allocate attention to postural con...
Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.
JAMA network open
Dec 1, 2020
IMPORTANCE: Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making.
Comparison of Use of Health Care Services and Spending for Unauthorized Immigrants vs Authorized Immigrants or US Citizens Using a Machine Learning Model.
JAMA network open
Dec 1, 2020
IMPORTANCE: Knowledge about use of health care services (health care utilization) and expenditures among unauthorized immigrant populations is uncertain because of limitations in ascertaining legal status in population data.
Dermal epidermal junction detection for full-field optical coherence tomography data of human skin by deep learning.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Nov 27, 2020
Full-field optical coherence tomography (FF-OCT) has been developed to obtain three-dimensional (3D) OCT data of human skin for early diagnosis of skin cancer. Detection of dermal epidermal junction (DEJ), where melanomas and basal cell carcinomas or...
Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey.
World neurosurgery
Nov 25, 2020
BACKGROUND: Artificial intelligence (AI) has the potential to disrupt how we diagnose and treat patients. Previous work by our group has demonstrated that the majority of patients and their relatives feel comfortable with the application of AI to aug...
Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.
Radiology
Nov 24, 2020
Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain. Purpose To demon...
Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study.
Indian heart journal
Nov 24, 2020
BACKGROUND: There is no large contemporary data from India to see the prevalence of burnout in HCWs in covid era. Burnout and mental stress is associated with electrocardiographic changes detectable by artificial intelligence (AI).
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