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

Floris Chabrun; Xavier Dieu; Pascal Reynier; Delphine Mirebeau-Prunier

doi:10.1093/clinchem/hvac145

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

Clin Chem. 2022 Oct 6;68(10):1341-1343. doi: 10.1093/clinchem/hvac145.

Authors

Floris Chabrun^{1

2}, Xavier Dieu^{1

2}, Pascal Reynier^{1

2}, Delphine Mirebeau-Prunier^{1

2}

Affiliations

¹ Laboratoire de Biochimie et Biologie Moléculaire, Centre Hospitalier Universitaire d'Angers, Angers 49933, France.
² Unité Mixte de Recherche (UMR) MITOVASC, Centre National de la Recherche Scientifique (CNRS) 6015, Institut National de la Santé et de la Recherche Médicale (INSERM) U1083, Université d'Angers, Angers 49035, France.

PMID: 36107955
DOI: 10.1093/clinchem/hvac145

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't
Letter
Comment

MeSH terms

Blood Proteins
Deep Learning*
Electrophoresis
Humans

Substances

Blood Proteins