AIMC Topic: Aged

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Utilizing artificial intelligence and medical experts to identify predictors for common diagnoses in dyspneic adults: A cross-sectional study of consecutive emergency department patients from Southern Sweden.

International journal of medical informatics
OBJECTIVE: Half of all adult emergency department (ED) visits with a complaint of dyspnea involve acute heart failure (AHF), exacerbation of chronic obstructive pulmonary disease (eCOPD), or pneumonia, which are often misdiagnosed. We aimed to create...

Comparison of CT referral justification using clinical decision support and large language models in a large European cohort.

European radiology
BACKGROUND: Ensuring appropriate use of CT scans is critical for patient safety and resource optimization. Decision support tools and artificial intelligence (AI), such as large language models (LLMs), have the potential to improve CT referral justif...

Analysis and characterization of extrachromosomal circular DNA in prostate cancer: Potential biomarker discovery from urine, plasma, and tumor samples.

Cancer letters
Extrachromosomal circular DNA (eccDNA) may contribute to genomic rearrangements and tumor heterogeneity, playing a role in cancer development and progression. This study evaluates eccDNA as a biomarker for prostate cancer by characterizing its profil...

Machine learning algorithms with body fluid parameters: an interpretable framework for malignant cell screening in cerebrospinal fluid.

Clinical chemistry and laboratory medicine
OBJECTIVES: This study aimed to develop and validate a machine learning (ML) model utilizing cerebrospinal fluid (CSF) body fluid parameters from hematology analyzers to screen for malignant cells.

Impact of analytical bias on machine learning models for sepsis prediction using laboratory data.

Clinical chemistry and laboratory medicine
OBJECTIVES: Machine learning (ML) models, using laboratory data, support early sepsis prediction. However, analytical bias in laboratory measurements can compromise their performance and validity in real-world settings. We aimed to evaluate how analy...

[The value of coronary angiography-derived fractional flow reserve and coronary angiography-derived index of microcirculatory resistance in coronary artery hemodynamic evaluation].

Zhonghua xin xue guan bing za zhi
To evaluate the diagnostic value of coronary angiography-derived fractional flow reserve (FFR) and index of microcirculatory resistance (IMR) for identifying coronary functional abnormalities. This diagnostic study enrolled patients with clinically...

MIA and CD163 as promising diagnostic biomarkers in vascular dementia: A multi-method study combining WGCNA, machine learning with validation in animal models and clinical samples.

International immunopharmacology
Vascular dementia (VaD), the second most common form of dementia, lacks reliable biomarkers for early diagnosis. Here, we integrated weighted gene co-expression network analysis (WGCNA) with machine learning to identify novel biomarkers and immune-me...

Enhancing pathological myopia diagnosis: a bimodal artificial intelligence approach integrating fundus and optical coherence tomography imaging for precise atrophy, traction and neovascularisation grading.

The British journal of ophthalmology
BACKGROUND: Pathological myopia (PM) has emerged as a leading cause of global visual impairment, early detection and precise grading of PM are crucial for timely intervention. The atrophy, traction and neovascularisation (ATN) system is applied to de...

Can polycythaemia vera disease be predicted from haematologic parameters? A machine learning-based study.

Journal of clinical pathology
AIMS: The aim of this research is to diagnose polycythaemia vera (PV) disease using different machine learning (ML) algorithms with complete blood count (CBC) parameters before further investigations such as Janus kinase 2 (), erythropoietin (EPO) an...