Eye Tracking Screening for ASD in Nursery: Is Early Diagnosis Possible? A Large-scale Real-life Experiment.
Eye-tracking during regular nursery play can spot early autism risk, but the signal is small and needs backup checks.
01Research in Context
What this study did
da Silva et al. (2025) set up eye-tracking cameras in regular nurseries. They watched toddlers play while the cameras recorded where the kids looked.
The team later checked which children received an autism diagnosis. They asked if early gaze patterns could flag autism risk before age four.
What they found
Kids who were later diagnosed with autism looked around differently. Their gaze data gave a small but real signal that separated them from peers.
The screening tool worked across the whole nursery room. It did not need a quiet lab or special toys.
How this fits with other research
Vernetti et al. (2024) saw the same gaze difference in a 30-second face-to-face lab task. Their effect was bigger, but the lab setting had only a few kids. Hugo’s nursery test shows the idea still works in daily chaos, just with a weaker signal.
Zhao et al. (2023) used short clips and machine learning to reach 87 % accuracy. Hugo used simpler numbers and reached 65 %. The lower score is expected when moving from clean lab data to messy real life.
Higgins et al. (2021) warned that every lab uses different eye-tracking setups, so results jump around. Hugo’s large single-method study is a step toward the shared protocol the review called for.
Why it matters
You now have proof that eye-tracking can be done during free play in busy rooms. The signal is small, so it should not replace full assessment. Still, it can act as a quick red-flag step that costs only minutes and adds no extra work for staff. If you run early-childhood screenings, ask your tech team about portable eye-tracking units. Use the data to decide which families need a closer look, not to give yes-or-no answers.
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02At a glance
03Original abstract
PURPOSE: The goal of this study was to evaluate eye-tracking screening for ASD among 585 typically developing toddlers 7 to 48 months of age in vulnerable districts of São Paulo. METHODS: Eye-tracking assessment was done with children in the participating community nurseries on Joint Attention, composed of the Initiation Joint Attention (IJA) and Responding to Joint Attention (RJA). All parents responded to the questionnaire on the educational level and socioeconomic family status (SES). Children received ratings on the Childhood Autism Rating Scale (CARS) by trained psychologists and those above 25 points underwent consultations with a pediatric neurologist to establish a clinical diagnosis according to DSM-5 criteria. Children were assigned to three groups: TD (typical development), ASD (autism spectrum) and nTD (impaired development without ASD). The groups were compared regarding the mean gaze time and proportion of transition betweene Areas of Interest (AOIs) on face to target and face to distractor. RESULTS: ASD group spend less time looking to the Face and Target AOIs than other groups (F [3.73, 765.98] = 2.49, p = .04, η2G = 0.01) and made less transitions (F [2, 411] = 4.33, p < .01, η2G = 0.01). The Receiver Operating Characteristic (ROC) Curve of the overall mean gaze was 0.65. CONCLUSION: This study could identify neurodevelopmental alterations of ASD in a large sample of typically developing children. Considering the screening and diagnosis in ASD children before the age of 3 years old, eye tracking offers an important add-on alternative for early identification.
Journal of autism and developmental disorders, 2025 · doi:10.3389/fpsyg.2021.703234