Machine learning-based algorithm as an innovative approach for the differentiation between diabetes insipidus and primary polydipsia in clinical practice

New insights on diagnosis and treatment of AVP deficiency

Arginine vasopressin deficiency (AVP-D) is one of the main entities of the polyuria-polydipsia syndrome. Its correct diagnosis and differentiation from the other two causes - AVP resistance and primary polydipsia - is crucial as this determines the further management of these patients.Over the last years, several new diagnostic tests using copeptin, the stable surrogate marker of AVP, have been introduced. Among them, hypertonic saline stimulated copeptin was confirmed to reliably and safely improve the diagnostic accuracy to diagnose AVP-D. Due to its simplicity, arginine stimulated copeptin was put forward as alternative test procedure. Glucagon-stimulated copeptin also showed promising results, while the oral growth hormone secretagogue Macimorelin failed to provide a sufficient stimulus. Interestingly, an approach using machine learning techniques also showed promising results concerning diagnostic accuracy.Once AVP-D is diagnosed, further workup is needed to evaluate its etiology. This will partly define the further treatment and management. In general, treatment of AVP-D focuses on desmopressin substitution, with oral formulations currently showing the best tolerance and safety profile. However, in addition to desmopressin substitution, recent data also showed that psychopathological factors play an important role in managing AVP-D patients.

Low-dimensional neural ODEs and their application in pharmacokinetics

Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.

Artificial intelligence in paediatric endocrinology: conflict or cooperation

Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.