Corrigendum: Advancement and independent validation of a deep learning-based tool for automated scoring of nail psoriasis severity using the modified nail psoriasis severity index

Front Med (Lausanne). 2025 Jun 5:12:1617441. doi: 10.3389/fmed.2025.1617441. eCollection 2025.

Stephan Kemenes^{1

2}, Liu Chang³, Maja Schlereth⁴, Rita Noversa de Sousa^{2

5}, Ioanna Minopoulou⁶, Pauline Fenzl^{2

5}, Giulia Corte^{2

5}, Melek Yalcin Mutlu^{2

5}, Michael Wolfgang Höner^{1

2}, Ioannis Sagonas^{1

2}, Birte Coppers^{2

5}, Anna-Maria Liphardt^{2

5}, David Simon⁶, Arnd Kleyer⁶, Lukas Folle³, Michael Sticherling^{1

2}, Georg Schett^{2

5}, Andreas Maier³, Filippo Fagni^{2

5}

¹ Department of Dermatology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
² Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
³ Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
⁴ Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
⁵ Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
⁶ Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Abstract

[This corrects the article DOI: 10.3389/fmed.2025.1574413.].

Keywords: MNAPSI; NAPSI; artificial intelligence; machine learning; nail disease; outcome measures; psoriasis; psoriatic arthritis.