AIMC Topic: Predictive Value of Tests

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Digital pathology and image analysis of p53 biomarker in lymphomas using two algorithms: correlation with genotype and visual inspection.

Journal of clinical pathology
p53 immunohistochemistry (IHC) is widely used as a rapid surrogate for detecting mutations, with mutations being a key biomarker for poor outcomes in lymphomas. We developed two algorithms using digital quantification tools to assess p53 expression...

Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk stratification in major depressive disorder in a prospective longitudinal study.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to play a role in its pathophysiology. Peripheral inflammatory biomarkers, such as the neutrophil-to-lymph...

Machine Learning Algorithms for Predicting Urinary Tract Infections: Integration of Demographic Data and Dipstick Reflectance Results.

Clinical chemistry
BACKGROUND: Urinary tract infections (UTIs) are among the most common infections encountered in healthcare settings. Current diagnostic practices often require 24-48 h due to the time needed for culture results. Given that 70%-80% of cultures return ...

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...

Artificial Intelligence Analysis of Chest Radiographs for Predicting Major Adverse Events in Patients Visiting the Emergency Department With Acute Cardiopulmonary Symptoms.

Korean journal of radiology
OBJECTIVE: In this study, we investigated whether artificial intelligence (AI) analysis of chest radiographs (CXRs) can predict major adverse clinical events in patients visiting the emergency department (ED) with acute cardiopulmonary symptoms.

Deep Learning-Enhanced CTA for Noninvasive Prediction of First Variceal Haemorrhage in Cirrhosis: A Multi-Centre Study.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIMS: The first variceal haemorrhage (FVH) is a life-threatening complication of liver cirrhosis that requires timely intervention; however, noninvasive tools for accurately predicting FVH remain limited. This study aimed to develop no...

Predicting arterial pressure without prejudice: towards effective hypotension prediction models.

British journal of anaesthesia
Selection bias has been identified in hypotension prediction models, but its impact on an algorithm's ability to learn relevant information from the arterial waveform remains unclear. The recent study by Yang and colleagues sheds considerable light o...

`Probabilistic ensemble learning for prediction of stroke thrombectomy outcomes from the NeuroVascular Quality Initiative-Quality Outcomes Database (NVQI-QOD) Acute Ischemic Stroke Registry.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
INTRODUCTION: Mechanical Thrombectomy (MT) is the standard of care in the interventional management of Acute Ischemic Stroke (AIS). The NVQI-QOD registry records detailed patient characteristics, pre-operative imaging, procedure metrics, and post-ope...

Utilizing Predictive Analytics to Understand Neurogenic Bladder Symptom Score (NBSS) Variations in Adults With Acquired Spinal Cord Injury.

Neurourology and urodynamics
INTRODUCTION: Individuals with spinal cord injury (SCI) have varying bladder health trajectories after their injury. We explored whether a predictive machine learning model could identify which variables impact urinary symptoms.

Diagnostic accuracy of an artificial intelligence-based platform in detecting periapical radiolucencies on cone-beam computed tomography scans of molars.

Journal of dentistry
OBJECTIVE: This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-based platform (Diagnocat) in detecting periapical radiolucencies (PARLs) in cone-beam computed tomography (CBCT) scans of molars. Specifically, we ...