Early Identification of Autism Using Cry Analysis: A Systematic Review and Meta-analysis of Retrospective and Prospective Studies.
Cry analysis is emerging as a promising tool for early autism identification. Acoustic features such as fundamental frequency (F0), cry duration, and phonation have shown potential as early vocal biomarkers. This systematic review and meta-analysis aimed to evaluate the diagnostic value of cry chara...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 56; no. 8; pp. 2950 - 2969 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195542267&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195542267 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Aug2026 vid: 56 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 195542267 183404487 195542267 195542267 10.1007/s10803-025-06757-4 195542267 ppf: 2950 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Early Identification of Autism Using Cry Analysis: A Systematic Review and Meta-analysis of Retrospective and Prospective Studies. aug: au: Pusil, Sandra Laguna, Ana Chino, Brenda Zegarra, Jonathan Adrián Orlandi, Silvia affil: https://ror.org/02f9zrr09 Zoundream AG, Novartis Campus - SIP Basel Area AG, Lichtstrasse 35, 4056, Basel, Switzerland sug: subj: Crying Evaluation Autism Spectrum Disorder Diagnosis Early Diagnosis Machine Learning Diagnosis, Computer Assisted Biological Markers Diagnostic Use Voice Quality Evaluation Pediatric Care Human Male Female Infant Child, Preschool Child Systematic Review Meta Analysis Embase Acoustics Phonation Analysis of Variance Neurodevelopment Infant Development Precision PubMed Psycinfo DSM Support Vector Machine Random Forest Confidence Intervals T-Tests Pearson's Correlation Coefficient Descriptive Statistics Data Analysis Software Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Male Female ab: Cry analysis is emerging as a promising tool for early autism identification. Acoustic features such as fundamental frequency (F0), cry duration, and phonation have shown potential as early vocal biomarkers. This systematic review and meta-analysis aimed to evaluate the diagnostic value of cry characteristics and the role of Machine Learning (ML) in improving autism screening. A comprehensive search of relevant databases was conducted to identify studies examining acoustic cry features in infants with an elevated likelihood of autism. Inclusion criteria focused on retrospective and prospective studies with clear cry feature extraction methods. A meta-analysis was performed to synthesize findings, particularly focusing on differences in F0, and assessing the role of ML-based cry analysis. The review identified eleven studies with consistent acoustic markers, including F0, phonation, duration, amplitude, and voice quality, as reliable indicators of neurodevelopmental differences associated with autism. ML approaches significantly improved screening precision by capturing non-linear patterns in cry data. The meta-analysis of six studies revealed a trend toward higher F0 in autistic infants, although the pooled effect size was not statistically significant. Methodological heterogeneity and small sample sizes were notable limitations across studies. Cry analysis holds promise as a non-invasive, accessible tool for early autism screening, with ML integration enhancing its diagnostic potential. However, the findings emphasize the need for large-scale, longitudinal studies with standardized methodologies to validate its utility and ensure its applicability across diverse populations. Addressing these gaps could establish cry analysis as a cornerstone of early autism identification. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|