AI Medical Compendium Journal:
Clinical biochemistry

Showing 1 to 10 of 12 articles

Shortcoming of serum B-cell maturation antigen measurement by enzyme-linked immunosorbent assay in one laboratory's experience: Unsatisfactory assay reproducibility.

Clinical biochemistry
INTRODUCTION: Serum protein electrophoresis and serum free light chain (SFLC) assays are standard methods for monitoring patients with multiple myeloma (MM). However, patients with non-secretory MM often require invasive bone marrow biopsies to monit...

Exploratory study of extracellular matrix biomarkers for non-invasive liver fibrosis staging: A machine learning approach with XGBoost and explainable AI.

Clinical biochemistry
BACKGROUND: Novel circulating markers for the non-invasive staging of chronic liver disease (CLD) are in high demand. Although underutilized, extracellular matrix (ECM) components offer significant diagnostic potential. This study evaluates ECM-relat...

Explainable artificial intelligence for LDL cholesterol prediction and classification.

Clinical biochemistry
INTRODUCTION: Monitoring LDL-C levels is essential in clinical practice because there is a direct relation between low-density lipoprotein cholesterol (LDL-C) levels and atherosclerotic heart disease risk. Therefore, measurement or estimate of LDL-C ...

Machine learning to improve false-positive results in the Dutch newborn screening for congenital hypothyroidism.

Clinical biochemistry
OBJECTIVE: The Dutch Congenital hypothyroidism (CH) Newborn Screening (NBS) algorithm for thyroidal and central congenital hypothyroidism (CH-T and CH-C, respectively) is primarily based on determination of thyroxine (T4) concentrations in dried bloo...

Reflex and reflective laboratory interventions for adding value to test results; an integral part of laboratory stewardship.

Clinical biochemistry
Post-analytical reflexive (automated) and/or reflective (patient tailored and thought driven) interventions (PARRI), have played a subsidiary role in many diagnostic laboratories, despite mounting evidence of their clinical value. The ever-pervasive ...

Applications of machine learning in routine laboratory medicine: Current state and future directions.

Clinical biochemistry
Machine learning is able to leverage large amounts of data to infer complex patterns that are otherwise beyond the capabilities of rule-based systems and human experts. Its application to laboratory medicine is particularly exciting, as laboratory te...

Staged reflexive artificial intelligence driven testing algorithms for early diagnosis of pituitary disorders.

Clinical biochemistry
BACKGROUND: Sellar masses (SM) frequently present with insidious hormonal dysfunction. We previously showed that, by utilizing a combined reflex/reflecting approach involving a laboratory clinician (LC) on common endocrine test results requested by n...

Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis.

Clinical biochemistry
OBJECTIVE: Lipid peroxidation constitutes a molecular mechanism involved in early Alzheimer Disease (AD) stages, and artificial neural network (ANN) analysis is a promising non-linear regression model, characterized by its high flexibility and utilit...

Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation.

Clinical biochemistry
Artificial intelligence (AI) and data science are rapidly developing in healthcare, as is their translation into laboratory medicine. Our review article presents an overview of the data science domain while discussing the reasons for its emergence. W...