Less is more: Antibiotics at the beginning of life

Antibiotic exposure at the beginning of life can lead to increased antimicrobial resistance and perturbations of the developing microbiome. Early-life microbiome disruption increases the risks of developing chronic diseases later in life. Fear of missing evolving neonatal sepsis is the key driver for antibiotic overtreatment early in life. Bias (a systemic deviation towards overtreatment) and noise (a random scatter) affect the decision-making process. In this perspective, we advocate for a factual approach quantifying the burden of treatment in relation to the burden of disease balancing antimicrobial stewardship and effective sepsis management

Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns

Necrotising enterocolitis (NEC) is one of the most common diseases in neonates and predominantly affects premature or very-low-birth-weight infants. Diagnosis is difficult and needed in hours since the first symptom onset for the best therapeutic effects. Artificial intelligence (AI) may play a significant role in NEC diagnosis. A literature search on the use of AI in the diagnosis of NEC was performed. Four databases (PubMed, Embase, arXiv, and IEEE Xplore) were searched with the appropriate MeSH terms. The search yielded 118 publications that were reduced to 8 after screening and checking for eligibility. Of the eight, five used classic machine learning (ML), and three were on the topic of deep ML. Most publications showed promising results. However, no publications with evident clinical benefits were found. Datasets used for training and testing AI systems were small and typically came from a single institution. The potential of AI to improve the diagnosis of NEC is evident. The body of literature on this topic is scarce, and more research in this area is needed, especially with a focus on clinical utility. Cross-institutional data for the training and testing of AI algorithms are required to make progress in this area