AIMC Topic: Disease Progression
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Rule-based natural language processing to examine variation in worsening heart failure hospitalizations by age, sex, race and ethnicity, and left ventricular ejection fraction.
American heart journal
Sep 7, 2024
BACKGROUND: Prior studies characterizing worsening heart failure events (WHFE) have been limited in using structured healthcare data from hospitalizations, and with little exploration of sociodemographic variation. The current study examined the impa...
Machine learning-enhanced electrical impedance myography to diagnose and track spinal muscular atrophy progression.
Physiological measurement
Sep 6, 2024
To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression.. Twenty-six infants with SMA and twenty-seven healthy infants had been enrolled and ...
Hybrid clinical-radiomics model based on fully automatic segmentation for predicting the early expansion of spontaneous intracerebral hemorrhage: A multi-center study.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Aug 31, 2024
BACKGROUND: Early prediction of hematoma expansion (HE) is important for the development of therapeutic strategies for spontaneous intracerebral hemorrhage (sICH). Radiomics can help to predict early hematoma expansion in intracerebral hemorrhage. Ho...
Deep Learning
China
Time Factors
Nomograms
Cerebral Hemorrhage
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Tomography, X-Ray Computed
Hematoma
Humans
Automation
Female
Decision Support Techniques
Reproducibility of Results
Male
Prognosis
Aged
Risk Factors
Middle Aged
Retrospective Studies
Aged, 80 and over
Risk Assessment
Radiomics
Disease Progression
Artificial intelligence for retinal diseases.
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Aug 27, 2024
PURPOSE: To discuss the worldwide applications and potential impact of artificial intelligence (AI) for the diagnosis, management and analysis of treatment outcomes of common retinal diseases.
Artificial intelligence in myopia in children: current trends and future directions.
Current opinion in ophthalmology
Aug 26, 2024
PURPOSE OF REVIEW: Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow my...
Metabolic signatures derived from whole-brain MR-spectroscopy identify early tumor progression in high-grade gliomas using machine learning.
Journal of neuro-oncology
Aug 24, 2024
PURPOSE: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resona...
The use of artificial neural networks in studying the progression of glaucoma.
Scientific reports
Aug 23, 2024
In ophthalmology, artificial intelligence methods show great promise due to their potential to enhance clinical observations with predictive capabilities and support physicians in diagnosing and treating patients. This paper focuses on modelling glau...
Machine Learning-Based Identification of Risk Factors of Keratoconus Progression Using Raw Corneal Tomography Data.
Cornea
Aug 20, 2024
PURPOSE: The purpose of this study was to identify early indicators of keratoconus progression in Pentacam data using machine learning (ML) techniques.
A future of AI-driven personalized care for people with multiple sclerosis.
Frontiers in immunology
Aug 19, 2024
Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to...
Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study.
Urology
Aug 15, 2024
OBJECTIVE: To construct and externally validate machine learning-based nomograms for predicting progression stages of initial prostate cancer (PCa) using biomarkers and clinicopathologic features.
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