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The human voice has the potential to serve as a valuable biomarker for the early detection, diagnosis, and monitoring of pediatric conditions. This scoping review synthesizes the current knowledge on the application of artificial intelligence (AI) in analyzing pediatric voice as a biomarker for health. The included studies featured voice recordings from pediatric populations aged 0–17 years, utilized feature extraction methods, and analyzed pathological biomarkers using AI models. Data from 62 studies were extracted, encompassing study and participant characteristics, recording sources, feature extraction methods, and AI models. Data from 39 models across 35 studies were evaluated for accuracy, sensitivity, and specificity. The review showed a global representation of pediatric voice studies, with a focus on developmental, respiratory, speech, and language conditions. The most frequently studied conditions were autism spectrum disorder, intellectual disabilities, asphyxia, and asthma. Mel-Frequency Cepstral Coefficients were the most utilized feature extraction method, while Support Vector Machines were the predominant AI model. The analysis of pediatric voice using AI demonstrates promise as a non-invasive, cost-effective biomarker for a broad spectrum of pediatric conditions. Further research is necessary to standardize the feature extraction methods and AI models utilized for the evaluation of pediatric voice as a biomarker for health. Standardization has significant potential to enhance the accuracy and applicability of these tools in clinical settings across a variety of conditions and voice recording types. Further development of this field has enormous potential for the creation of innovative diagnostic tools and interventions for pediatric populations globally.

Abstract Background Growing evidence suggests low and high maternal hemoglobin (Hb) concentrations may have adverse consequences for maternal and child health. There remain questions on specific Hb thresholds to define anemia and high Hb as well as how cutoffs may vary by anemia etiology and timing of assessment. Methods We conducted an updated systematic review (using PubMed and Cochrane Review) on low (

Abstract Child undernutrition in Cambodia is a persistent public health problem requiring low‐cost and scalable solutions. Rising cellphone use in low‐resource settings represents an opportunity to replace in‐person counselling visits with phone calls; however, questions remain on relative effectiveness. Our objective was to evaluate the impact of two options for delivering a World Vision infant and young child feeding (IYCF) counselling programme: (1) traditional Positive Deviance/Hearth (PDH) programme with in‐person visits or (2) PDH with Interactive Voice Calling (PDH‐IVC) which integrates phone calls to replace 62.5% of face‐to‐face interaction between caregivers and volunteers, compared to the standard of care (SOC). We conducted a longitudinal cluster‐randomised controlled trial in 361 children 6–23 months. We used an adjusted difference‐in‐difference approach using baseline, midline (3 months) and endline (12 months) surveys to evaluate the impact on child growth among the three groups. At baseline, nearly a third of children were underweight, and over half were food insecure. At midline the PDH group and the PDH‐IVC groups had improved weight‐for‐age z‐scores (0.13 DID, p = 0.011; 0.13 DID, p = 0.02, respectively) and weight‐for‐height z‐score (0.16 DID, p = 0.038; 0.24 DID, p = 0.002), relative to SOC. There were no differences in child height‐for‐age z‐scores. At endline, the impact was sustained only in the PDH‐IVC group for weight‐for‐age z‐score (0.14 DID, p = 0.049), and the prevalence of underweight declined by 12.8 percentage points (p = 0.036), relative to SOC. Integration of phone‐based IYCF counselling is a potentially promising solution to reduce the burden of in‐person visits; however, the modest improvements suggest the need to combine it with other strategies to improve child nutrition.