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In Journal of clinical research in pediatric endocrinology

Assessment and management of children with growth failure has improved greatly over recent years. However, there remains a strong potential for further improvements by use of novel digital techniques. A panel of experts discussed developments in digitalization of a number of important tools used by pediatric endocrinologists at the third 360° European Meeting on Growth and Endocrine Disorders, funded by Merck KGaA, Germany, and this review is based on those discussions. It was reported that electronic monitoring and new algorithms have been devised that are providing more sensitive referral for short stature, and computer programs have improved ways in which diagnoses are coded for use by various groups such as healthcare providers and government health systems. Innovative cranial imaging techniques have been devised that are considered safer than using gadolinium contrast agents and are also more sensitive and accurate. Deep-learning neural networks are changing the way that bone age and bone health are assessed, which are more objective than standard methodologies. Models for prediction of growth response to GH treatment are being improved by applying novel artificial intelligence methods that can identify non-linear and linear factors that relate to response, providing more accurate predictions. Determination and interpretation of IGF-I levels are becoming more standardized and consistent, for evaluation across different patient groups, and computer-learning models indicate that baseline IGF-I SDS is among the most important indicators of GH therapy response. While physicians involved in child growth and treatment of disorders resulting in growth failure need to be aware of and keep abreast of these latest developments, treatment decisions and management should continue to be based on clinical decisions. New digital technologies and advancements in the field should be aimed at making clinical decisions more efficient and consider patient-centered approaches. Keywords: Short stature, height monitoring, bone age, cranial imaging, growth hormone treatment, prediction models.

Labarta José I, Ranke Michael B, Maghnie Mohamad, Martin David, Guazzarotti Laura, Pfäffle Roland, Koledova Ekaterina, Wit Jan M

2020-Oct-02