Analysis of nailfold capillaroscopy images with artificial intelligence: Data from literature and performance of machine learning and deep learning from images acquired in the SCLEROCAP study.

Journal: Microvascular research

PMID: 39389419

Abstract

OBJECTIVE: To evaluate the performance of machine learning and then deep learning to detect a systemic scleroderma (SSc) landscape from the same set of nailfold capillaroscopy (NC) images from the French prospective multicenter observational study SCLEROCAP.

Authors

Lutfi Ozturk

CHU de Saint-Etienne, Médecine Vasculaire et Thérapeutique, Saint-Etienne, France. Electronic address: lutfi.ozturk@chu-st-etienne.fr.
Charlotte Laclau

Université Jean Monnet, Laboratoire Hubert Curien, Saint-Etienne, France.
Carine Boulon

CHU St-André, Médecine Vasculaire, Bordeaux, France.
Marion Mangin

CHU St-André, Médecine Vasculaire, Bordeaux, France.
Etheve Braz-Ma

Université Jean Monnet, Laboratoire Hubert Curien, Saint-Etienne, France.
Joel Constans

CHU St-André, Médecine Vasculaire, Bordeaux, France.
Loubna Dari

CHU St-André, Médecine Vasculaire, Bordeaux, France.
Claire Le Hello

CHU de Saint-Etienne, Médecine Vasculaire et Thérapeutique, Saint-Etienne, France; Université Jean Monnet, CHU Saint-Etienne, Médecine Vasculaire et Thérapeutique, Mines Saint-Etienne, INSERM, SAINBIOSE U1059, Saint-Etienne, France.

Keywords

Adult Aged Deep Learning Female France Humans Image Interpretation, Computer-Assisted Male Microscopic Angioscopy Middle Aged Nails Predictive Value of Tests Prospective Studies Reproducibility of Results Scleroderma, Systemic Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (39389419)

Analysis of nailfold capillaroscopy images with artificial intelligence: Data from literature and performance of machine learning and deep learning from images acquired in the SCLEROCAP study.

Abstract

Authors

Keywords

External Resources

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