AI Medical Compendium Journal:
Clinical chemistry

Showing 11 to 20 of 33 articles

Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data.

Clinical chemistry
BACKGROUND: Hemoglobinopathies, the most common inherited blood disorder, are frequently underdiagnosed. Early identification of carriers is important for genetic counseling of couples at risk. The aim of this study was to develop and validate a nove...

Automating the Detection of IV Fluid Contamination Using Unsupervised Machine Learning.

Clinical chemistry
BACKGROUND: Intravenous (IV) fluid contamination is a common cause of preanalytical error that can delay or misguide treatment decisions, leading to patient harm. Current approaches for detecting contamination rely on delta checks, which require a pr...

Applications of Artificial Intelligence in Urinalysis: Is the Future Already Here?

Clinical chemistry
BACKGROUND: Artificial intelligence (AI) has emerged as a promising and transformative tool in the field of urinalysis, offering substantial potential for advancements in disease diagnosis and the development of predictive models for monitoring medic...

Expert-Level Immunofixation Electrophoresis Image Recognition based on Explainable and Generalizable Deep Learning.

Clinical chemistry
BACKGROUND: Immunofixation electrophoresis (IFE) is important for diagnosis of plasma cell disorders (PCDs). Manual analysis of IFE images is time-consuming and potentially subjective. An artificial intelligence (AI) system for automatic and accurate...

In Reply to Performance of Deep Learning in the Interpretation of Serum Protein Electrophoresis.

Clinical chemistry
We thank He et al. for their comments on our article (1), which gives us the opportunity to clarify some methodological points. 1. Detection of abnormal patterns: mechanics.