About this Research Topic

Background

Due to progress in digital data collection techniques as well as machine learning and computing infrastructure, artificial intelligence (AI) applications are more and more expanding into areas that were previously thought to be only reserved to human beings. These areas also include medicine. By offering manifold possibilities for optimising clinical routines and improving the quality of life of patients, AI is gradually changing healthcare. On the one hand, AI tools can be used to (earlier) detect diseases, to automatically track the progression of diseases over time, or to even prevent diseases. On the other hand, AI can help to optimise therapy. At the moment, research in AI for healthcare mainly concentrates on cancer, nervous system diseases, and cardiovascular diseases. In recent years, AI has also found its way into healthcare for children.



Compared to other medical fields, AI research is clearly underrepresented in child healthcare. Results from existing studies, however, are promising with regard to the earlier, faster, cheaper, and less invasive detection of diseases in this vulnerable group of individuals. Moreover, AI has a huge, but to date hardly utilised, potential with regard to individualised intervention that is less bound to stays in hospitals. To boost further research in these fields, this research topic targets on high-quality contributions to (1) the automated analysis and classification of anatomical structures, physiological processes and behavioural patterns in children, i.e., human individuals from their prenatal period to adolescence, (2) the automated detection of various pathological changes in children, and (3) the AI-based intervention for children.



Original articles and brief research reports focusing on AI methodology applied in the context of healthcare and wellbeing for human individuals at a young age, i.e., at a time from their prenatal period until adolescence, may be submitted. We also encourage the submission of topic-related mini review articles, review articles, systematic review articles, policy and practice review articles, methods articles, data reports, opinion articles, and perspectives articles.



Topics of interest include but are not limited to:



• Innovative AI-based studies on a variety of medical conditions such as developmental disorders, mood disorders, genetic disorders, infectious diseases, cerebral palsy, plus disease, and pathological anatomical changes

• Automatic approaches for the earlier detection of developmental disorders that are currently diagnosed at toddlerhood or beyond such as autism spectrum disorder

• Comparison of automatic disease detection approaches with standard clinical assessments

• Application of machine learning methods to follow children affected by pre-, peri-, and postnatal complications such as preeclampsia, maternal substance abuse during pregnancy, preterm birth, very low birth weight, neonatal asphyxia, and cerebral haemorrhage that pose them at heightened risk for developmental deviances

• Automatic detection of medical conditions based on early vocalisations or early human motor behaviour

• Deep learning techniques for child data processing in medical context

• Development and evaluation of novel AI-based intervention approaches for children with a medical condition

• Intelligent robot-based intervention for children with autism spectrum disorder

• Automatic child emotion recognition within a medical/therapeutic context

• Fairness and explainability of AI in context of child health and wellbeing

• Discussion of ethical and methodological issues related to AI approaches for child healthcare and wellbeing

Keywords: artificial intelligence, child health, pediatrics, deep learning, autism, machine learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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