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touchMEETING HIGHLIGHTS
Spotlight on clinical management of growth hormone disorders through AI & digital innovation
Learning Objectives
After watching this activity, participants should be better able to:
- Outline how AI is changing paediatric endocrinology including its impact on personalised treatment plans, improving accessibility and how it is being incorporated into everyday practice
- Describe key components of the digital health ecosystem for managing paediatric growth hormone disorders, including the contribution of predictive modelling and how it translates into improved clinical outcomes
- Highlight how AI-based data science can enhance the accuracy of growth hormone disorders response predictions in paediatric endocrine disorders
Overview
The development of AI offers the potential to transform the treatment of disease. In the management of paediatric patients with endocrine disorders, integrating AI may lead to several benefits including increased diagnostic accuracy facilitating early intervention, personalised treatment plans, improved access to treatment, continuous monitoring and improved prediction of treatment responses.
In this activity, watch four leading experts outline the current impact of evidence based digital health solutions in the management of paediatric endocrine disorders.
References
- Araújo M, Van Dommelen P, Srivastava J, Koledova E. A data-driven intervention framework for improving adherence to growth hormone therapy based on clustering analysis and traffic light alerting systems. Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era 2021 (pp. 23-27).
- Araujo M, van Dommelen P, Koledova E, Srivastava J. Using deep learning for individual-level predictions of adherence with growth hormone therapy. Public Health and Informatics 2021 (pp. 133-137).
- Bang P, Ahmed SF, Argente J, Backeljauw P, Bettendorf M, Bona G, Coutant R, Rosenfeld RG, Walenkamp MJ, Savage MO. Identification and management of poor response to growth‐promoting therapy in children with short stature. Clinical endocrinology. 2012 Aug;77(2):169-81.
- Bidlingmaier M, Gleeson H, Latronico AC, Savage MO. Applying precision medicine to the diagnosis and management of endocrine disorders. Endocrine Connections. 2022 Oct 1;11(10).
- Fernandez-Luque L, Al Herbish A, Al Shammari R, Argente J, Bin-Abbas B, Deeb A, Dixon D, Zary N, Koledova E, Savage MO. Digital health for supporting precision medicine in pediatric endocrine disorders: opportunities for improved patient care. Frontiers in pediatrics. 2021 Jul 29;9:715705.
- Koledova E, Le Masne Q, Spataru A, Bagha M, Dixon D. Digital Health in the Management of Pediatric Growth Hormone Therapy–10 Years of Developments. Public Health and Informatics 2021 (pp. 926-930).
- Kuczmarski RJ. 2000 CDC Growth Charts for the United States: methods and development. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. 2002.
- Spataru A, van Dommelen P, Arnaud L, Masne QL, Quarteroni S, Koledova EB. A Machine Learning Approach for Identifying Children at Risk of Suboptimal Adherence to Growth Hormone Therapy. Journal of the Endocrine Society. 2021 Apr 1;5(Supplement_1):A672-3.
- Stevens A, Murray P, De Leonibus C, Garner T, Koledova E, Ambler G, Kapelari K, Binder G, Maghnie M, Zucchini S, Bashnina E. Gene expression signatures predict response to therapy with growth hormone. The Pharmacogenomics Journal. 2021 Oct;21(5):594-607.
- Tornincasa V, Dixon D, Le Masne Q, Martin B, Arnaud L, van Dommelen P, Koledova E. Integrated digital health solutions in the management of growth disorders in pediatric patients receiving growth hormone therapy: a retrospective analysis. Frontiers in Endocrinology. 2022 Jun 30;13:882192.
- Van Dommelen P, Arnaud L, Koledova E. Curve matching to predict growth in patients receiving growth hormone therapy: An interpretable & explainable method. Frontiers in Endocrinology. 2022 Oct 5;13:999077.
- Zou P, Zhang L, Zhang R, Wang C, Lin X, Lai C, Lu Y, Yan Z. Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter‐Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls. Journal of Magnetic Resonance Imaging. 2023 Dec;58(6):1977-87.
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