AIMC Topic: Cross-Sectional Studies

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Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. Accurate detection is key to successful interventions, but diagnosis of sepsis is complicated becaus...

Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey.

Journal of medical Internet research
BACKGROUND: Considerable research is being conducted as to how artificial intelligence (AI) can be effectively applied to health care. However, for the successful implementation of AI, large amounts of health data are required for training and testin...

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.

Scientific reports
Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy ...

Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.

Scientific reports
Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regre...

S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.

IEEE transactions on medical imaging
Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to faci...

Machine learning to predict distal caries in mandibular second molars associated with impacted third molars.

Scientific reports
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occ...

Automatic segmentation of corneal deposits from corneal stromal dystrophy images via deep learning.

Computers in biology and medicine
BACKGROUND: Granular dystrophy is the most common stromal dystrophy. To perform automated segmentation of corneal stromal deposits, we trained and tested a deep learning (DL) algorithm from patients with corneal stromal dystrophy and compared its per...

Needs for re-intervention on restored teeth in adults: a practice-based study.

Clinical oral investigations
OBJECTIVES: Evaluate the need for re-intervention on dental coronal restorations in adults seen in a network of general dental practitioners (ReCOL).  MATERIALS AND METHODS: This observational, cross-sectional, multicenter study involved 40 practitio...