Aplasia cutis congenita of extremities (group VII): case report from North-Western Nigeria.

Aplasia cutis congenita (ACC) is a rare congenital disorder characterised by localised or widespread absence of skin mainly affecting the scalp. Bilateral involvement of both the upper and lower extremities is uncommon. This is a case report of a rare congenital disorder. The patient was a 26-hour-old male baby admitted with extensive absence of skin on the lower and upper extremities. He was co-managed conservatively with the plastic surgical team for ACC group VII. The lesions were healing satisfactorily until 12 days into the admission when the parents signed against medical advice despite counselling. ACC with involvement of both upper and lower extremities is a rare presentation that responds to conservative treatment. The report emphasises the need for a legal framework for physicians to override the decision of the caregiver not in the best interest of a child.

Machine Learning for Child and Adolescent Health: A Systematic Review.

CONTEXT: In the last few decades, data acquisition and processing has seen tremendous amount of growth, thus sparking interest in machine learning (ML) within the health care system. OBJECTIVE: Our aim for this review is to provide an evidence map of the current available evidence on ML in pediatrics and adolescent medicine and provide insight for future research. DATA SOURCES: A literature search was conducted by using Medline, the Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature Plus, Web of Science Library, and EBSCO Dentistry & Oral Science Source. STUDY SELECTION: Articles in which an ML model was assessed for the diagnosis, prediction, or management of any condition in children and adolescents (0-18 years) were included. DATA EXTRACTION: Data were extracted for year of publication, geographical location, age range, number of participants, disease or condition under investigation, study methodology, reference standard, type, category, and performance of ML algorithms. RESULTS: The review included 363 studies, with subspecialties such as psychiatry, neonatology, and neurology having the most literature. A majority of the studies were from high-income (82%; n = 296) and upper middle-income countries (15%; n = 56), whereas only 3% (n = 11) were from low middle-income countries. Neural networks and ensemble methods were most commonly tested in the 1990s, whereas deep learning and clustering emerged rapidly in the current decade. LIMITATIONS: Only studies conducted in the English language could be used in this review. CONCLUSIONS: The interest in ML has been growing across various subspecialties and countries, suggesting a potential role in health service delivery for children and adolescents in the years to come.