Evidence-Based Growth Monitoring (EBGM)

The EBGM project, led by Pauline Scherdel, aims to externally evaluate and refine an artificial intelligence (AI) algorithm for detecting abnormal growth in children. Initially, we developed and internally evaluated an algorithm for children aged between 1 and 12 years for detecting two target and priority conditions: Turner syndrome and growth hormone deficiency. The AI algorithm shows good diagnostic performance, achieving a sensitivity of over 90%, a specificity of around 95%, and a median theoretical reduction in diagnostic time of 2 to 4 years.  The aim is to externally evaluate this AI algorithm using data of diseased children from the AP-HP clinical data warehouse and apparently healthy children included in the ELFE national mother-child cohort.

Implementing a personalized and validated AI algorithm into primary care physicians' software will optimize growth monitoring by improving the prognosis of severe health conditions and reducing futile referrals for specialist consultations.