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Area of Science:

  • Pediatric neurology
  • Computational neuroscience
  • Developmental psychology

Background:

  • Early autism spectrum disorder (ASD) screening is crucial for timely diagnosis and intervention, improving long-term prognosis.
  • Traditional screening tools like the Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) often require time-consuming follow-up and are prone to human scoring errors.
  • Barriers such as limited time and training can hinder widespread pediatric ASD screening.

Purpose of the Study:

  • To evaluate an automated machine learning (ML) method, specifically a feedforward neural network (fNN), for ASD screening.
  • To determine if ML can overcome the limitations of traditional ASD screening methods, including scoring accuracy and efficiency.
  • To assess the performance of the fNN model across diverse demographic subgroups.

Main Methods:

  • Utilized archival M-CHAT-R data from 14,995 toddlers (aged 16-30 months).
  • The 20 M-CHAT-R items served as inputs for the fNN model, with ASD diagnosis as the output.
  • Analyzed performance across subgroups based on race, sex, and maternal education levels.

Main Results:

  • The fNN model achieved high correct classification rates, with the best results reaching 99.72% for the total sample using 18 items.
  • Performance remained strong across subgroups: 99.92% for white toddlers (14 items), 99.79% for black toddlers (18 items), 99.64% for boys (18 items), and 99.95% for girls (18 items).
  • Models using 16 items achieved 99.75% and 99.70% accuracy for toddlers with lower and higher maternal education, respectively.

Conclusions:

  • The ML-based fNN method demonstrates comparable accuracy to the M-CHAT-R with follow-up items but requires fewer questions.
  • This automated approach offers efficient scoring, reduces the need for labor-intensive follow-up, and minimizes human error.
  • ML presents a promising, advantageous alternative to traditional ASD screening methods, enhancing diagnostic efficiency and accessibility.