AI Medical Compendium Topic

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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway.

BMC cancer
BACKGROUND: Exosome small RNAs are believed to be involved in the pathogenesis of cancer, but their role in breast cancer is still unclear. This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated th...

Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence.

Biomedical physics & engineering express
Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts ea...

Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.

Scientific reports
Brain metastases (BMs) in extensive-stage small cell lung cancer (ES-SCLC) are often associated with poor survival rates and quality of life, making the timely identification of high-risk patients for BMs in ES-SCLC crucial. Patients diagnosed with E...

Predictive model of in-hospital mortality in liver cirrhosis patients with hyponatremia: an artificial neural network approach.

Scientific reports
Hyponatremia can worsen the outcomes of patients with liver cirrhosis. However, it remains unclear about how to predict the risk of death in cirrhotic patients with hyponatremia. Patients with liver cirrhosis and hyponatremia were screened. Eligible ...

Machine learning-based prediction of elevated N terminal pro brain natriuretic peptide among US general population.

ESC heart failure
AIMS: Natriuretic peptide-based pre-heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre...

Using machine learning to identify pediatric ophthalmologists.

Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus
This cross-sectional study used data from the American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) and machine learning algorithms to identify pediatric ophthalmologists based on physician coding patterns. A random forest m...

Machine learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predicting need for early critical care.

CJEM
STUDY OBJECTIVE: This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critic...

Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients.

Scientific reports
Knee osteoarthritis (KOA) combined with depressive symptoms is prevalent and leads to poor outcomes and significant financial burdens. However, practical tools for identifying at-risk patients remain limited. A robust prediction model is needed to ad...

Machine learning model-based preterm birth prediction and clinical nomogram: A big retrospective cohort study.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: This study sought to develop a multifactorial predictive model for preterm birth risk, with the goal of providing clinical practitioners with early prevention.

Comparative efficacy of anteroposterior and lateral X-ray based deep learning in the detection of osteoporotic vertebral compression fracture.

Scientific reports
Magnetic resonance imaging remains the gold standard for diagnosing osteoporotic vertebral compression fractures (OVCF), but the use of X-ray imaging, particularly anteroposterior (AP) and lateral views, is prevalent due to its accessibility and cost...