Convolutional neural networks for automatic MR classification of myocardial iron overload in thalassemia major patients.

Journal: European radiology

PMID: 39658686

OBJECTIVES: To develop a deep-learning model for supervised classification of myocardial iron overload (MIO) from magnitude T2* multi-echo MR images.

Vincenzo Positano

Fondazione Toscana "G. Monasterio", Pisa, Italy.
Antonella Meloni

Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy.
Lisa Anita De Santi

University of Pisa, Pisa, Italy.
Laura Pistoia

Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy.
Zelia Borsellino

Unità Operativa Complessa Ematologia con Talassemia, ARNAS Civico "Benfratelli-Di Cristina", Palermo, Italy.
Alberto Cossu

Child Neuropsychiatry Unit, Department of Children and Maternal Health, AOUI Verona, full member of European Reference Network EpiCARE, Verona, Italy.
Francesco Massei

UO Oncoematologia Pediatrica, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy.
Paola Maria Grazia Sanna

Servizio trasfusionale aziendale, Azienda Ospedaliero Universitaria di Sassari, Sassari, Italy.
Maria Filomena Santarelli

CNR Institute of Clinical Physiology, CNR Research Area-Via Moruzzi, 1, 56124, Pisa, Italy. mariafilomena.santarelli@cnr.it.
Filippo Cademartiri

Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy.

Adolescent Adult beta-Thalassemia Child Convolutional Neural Networks Deep Learning Female Humans Image Interpretation, Computer-Assisted Iron Overload Magnetic Resonance Imaging Male Neural Networks, Computer Retrospective Studies Sensitivity and Specificity Young Adult

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