Meeting Banner
Abstract #2519

A machine learning model for identifying idiopathic central precocious puberty in girls based on medical images and clinical multi-parameters

Yi Lu1, PinFa Zou1, LingFeng Zhang1, lu han2, and Zhihan Yan1
1Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China, 2Philips Healthcare, Shanghai, China

Synopsis

Keywords: Adolescents, Endocrine

Early identification of precocious puberty (PP) is important to guarantee the growth and development of children. The aim of this study was to propose a robust machine learning model that incorporate information from pituitary MRI images, carpal bone age, gonadal ultrasound, baseline sex hormone tests, and clinical information to identify idiopathic central precocious puberty (ICPP). The experiments show that the AUCs are 0.860, 0.862, and 0.866 respectively for the training set, internal validation sets, and external validation sets. The performance suggests that the presented model could be an alternative clinical approach.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords