AIMC Topic: Case-Control Studies
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Diagnosis of multiple sclerosis using optical coherence tomography supported by explainable artificial intelligence.
Eye (London, England)
Jan 31, 2024
BACKGROUND/OBJECTIVES: Study of retinal structure based on optical coherence tomography (OCT) data can facilitate early diagnosis of relapsing-remitting multiple sclerosis (RRMS). Although artificial intelligence can provide highly reliable diagnoses...
Predicting Penicillin Allergy: A United States Multicenter Retrospective Study.
The journal of allergy and clinical immunology. In practice
Jan 17, 2024
BACKGROUND: Using the reaction history in logistic regression and machine learning (ML) models to predict penicillin allergy has been reported based on non-US data.
Time-domain heart rate dynamics in the prognosis of progressive atherosclerosis.
Nutrition, metabolism, and cardiovascular diseases : NMCD
Jan 14, 2024
BACKGROUND AND AIM: The regular uptake of a high-fat diet (HFD) with changing lifestyle causes atherosclerosis leading to cardiovascular diseases and autonomic dysfunction. Therefore, the current study aimed to investigate the correlation of autonomi...
Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.
Journal of the American Geriatrics Society
Jan 13, 2024
BACKGROUND: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screen...
Seeing through a robot's eyes: A cross-sectional exploratory study in developing a robotic screening technology for autism.
Autism research : official journal of the International Society for Autism Research
Jan 6, 2024
The present exploratory cross-sectional case-control study sought to develop a reliable and scalable screening tool for autism using a social robot. The robot HUMANE, installed with computer vision and linked with recognition technology, detected the...
Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study.
Acta radiologica (Stockholm, Sweden : 1987)
Dec 19, 2023
BACKGROUND: Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system.
Development and Validation of a Deep-Learning Model to Predict Total Hip Replacement on Radiographs: The Total Hip Replacement Prediction (THREP) Model.
The Journal of bone and joint surgery. American volume
Dec 12, 2023
BACKGROUND: There are few methods for accurately assessing the risk of total hip arthroplasty (THA) in patients with osteoarthritis. A novel and reliable method that could play a substantial role in research and clinical routine should be investigate...
Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.
Journal of the Formosan Medical Association = Taiwan yi zhi
Dec 2, 2023
BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.
Humans
Neural Networks, Computer
Machine Learning
Female
Aged
Aged, 80 and over
Mitochondrial Precursor Protein Import Complex Proteins
Sensitivity and Specificity
Polymorphism, Single Nucleotide
Support Vector Machine
Alzheimer Disease
Male
Gene-Environment Interaction
Case-Control Studies
ROC Curve
Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.
BJOG : an international journal of obstetrics and gynaecology
Nov 20, 2023
OBJECTIVES: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these m...
Artificial intelligence for detecting keratoconus.
The Cochrane database of systematic reviews
Nov 15, 2023
BACKGROUND: Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on clinical examination and corneal imaging; though in the early stages, ...
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