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Early Identification of Autism Using Cry Analysis: A Systematic Review and Meta-analysis of Retrospective and Prospective Studies.

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Title: Early Identification of Autism Using Cry Analysis: A Systematic Review and Meta-analysis of Retrospective and Prospective Studies.
Authors: Pusil, Sandra (AUTHOR), Laguna, Ana (AUTHOR), Chino, Brenda (AUTHOR), Zegarra, Jonathan Adrián (AUTHOR), Orlandi, Silvia (AUTHOR)
Source: Journal of Autism & Developmental Disorders. Aug2026, Vol. 56 Issue 8, p2950-2968. 19p.
Subjects: Diagnosis of autism, Crying, Medical information storage & retrieval systems, Random forest algorithms, Pearson correlation (Statistics), Infant development, T-test (Statistics), Acoustics, Neural development, Meta-analysis, Classification of mental disorders, Descriptive statistics, Pediatrics, Systematic reviews, MEDLINE, Support vector machines, Computer-aided diagnosis, Analysis of variance, Asperger's syndrome, Early diagnosis, Machine learning, Human voice, Accuracy, Online information services, Confidence intervals, Data analysis software, Biomarkers, Psychology information storage & retrieval systems, Children
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Early Identification of Autism Using Cry Analysis: A Systematic Review and Meta-analysis of Retrospective and Prospective Studies.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Autism+%26+Developmental+Disorders%22">Journal of Autism & Developmental Disorders</searchLink>. Aug2026, Vol. 56 Issue 8, p2950-2968. 19p.
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10803-025-06757-4
    Languages:
      – Code: eng
        Text: English
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    Subjects:
      – SubjectFull: Diagnosis of autism
        Type: general
      – SubjectFull: Crying
        Type: general
      – SubjectFull: Medical information storage & retrieval systems
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Pearson correlation (Statistics)
        Type: general
      – SubjectFull: Infant development
        Type: general
      – SubjectFull: T-test (Statistics)
        Type: general
      – SubjectFull: Acoustics
        Type: general
      – SubjectFull: Neural development
        Type: general
      – SubjectFull: Meta-analysis
        Type: general
      – SubjectFull: Classification of mental disorders
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Pediatrics
        Type: general
      – SubjectFull: Systematic reviews
        Type: general
      – SubjectFull: MEDLINE
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      – SubjectFull: Support vector machines
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      – SubjectFull: Computer-aided diagnosis
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      – SubjectFull: Early diagnosis
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      – SubjectFull: Human voice
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      – SubjectFull: Biomarkers
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      – SubjectFull: Psychology information storage & retrieval systems
        Type: general
      – SubjectFull: Children
        Type: general
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      – TitleFull: Early Identification of Autism Using Cry Analysis: A Systematic Review and Meta-analysis of Retrospective and Prospective Studies.
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              Text: Aug2026
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