AIMC Topic: Longitudinal Studies
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Detecting changes in the performance of a clinical machine learning tool over time.
EBioMedicine
Oct 2, 2023
BACKGROUND: Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a m...
A deep sift convolutional neural networks for total brain volume estimation from 3D ultrasound images.
Computer methods and programs in biomedicine
Sep 17, 2023
Preterm infants are a highly vulnerable population. The total brain volume (TBV) of these infants can be accurately estimated by brain ultrasound (US) imaging which enables a longitudinal study of early brain growth during Neonatal Intensive Care (NI...
Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment.
European archives of psychiatry and clinical neuroscience
Sep 16, 2023
Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can dist...
Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition.
Scientific reports
Jul 19, 2023
This study uses artificial intelligence for testing (1) whether the comorbidity of diabetes and its comorbid condition is very strong in the middle-aged or old (hypothesis 1) and (2) whether major determinants of the comorbidity are similar for diffe...
The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach.
Acta ophthalmologica
Jun 10, 2023
PURPOSE: The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer-aided method to facilitate diagnosis and prognosis in MS.
Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.
Scientific reports
Jun 10, 2023
Machine learning (ML) could have advantages over traditional statistical models in identifying risk factors. Using ML algorithms, our objective was to identify the most important variables associated with mortality after dementia diagnosis in the Swe...
A deep learning framework for personalised dynamic diagnosis of graft fibrosis after liver transplantation: a retrospective, single Canadian centre, longitudinal study.
The Lancet. Digital health
May 18, 2023
BACKGROUND: Recurrent graft fibrosis after liver transplantation can threaten both graft and patient survival. Therefore, early detection of fibrosis is essential to avoid disease progression and the need for retransplantation. Non-invasive blood-bas...
A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease.
Artificial intelligence in medicine
Apr 7, 2023
PURPOSE: Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that machine learning (ML) techniques have attempted to resolve in the last decade. This study introduces a first-of-its-kind color-coded visualization mec...
Re: The Evolution of Retzius-Sparing Robot-Assisted Laparoscopic Prostatectomy: An Idea, Development, Evolution, Assessment, and Long-Term Study Perspective (From Gotlieb et al., J Endourol 2022;36(8):1077-1082).
Journal of endourology
Mar 10, 2023
Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.
Journal of medical Internet research
Feb 16, 2023
BACKGROUND: An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19.
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