ArticleNature communications2020
Detection of eye contact with deep neural networks is as accurate as human experts.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.Autism research : official journal of the International Society for Autism Research · 2026Pooled it
- Artificial intelligence for tracking social behaviours and supporting an autism spectrum disorder diagnosis: systematic review and meta-analysis.EBioMedicine · 2025Pooled it
- Automatic measurement of social gaze during naturalistic conversations in autism.Journal of psychiatric research · 2026Article
- Attention deficit hyperactivity disorder assessment through objective measures: POV glasses and machine learning approach.Frontiers in psychiatry · 2026Article
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- Reliably quantifying the severity of social symptoms in children with autism using ASDSpeech.Translational psychiatry · 2025Article
- Objective Measurement of Social Gaze and Smile Behaviors in Children with Suspected Autism Spectrum Disorder During Administration of the Autism Diagnostic Observation Schedule, 2nd Edition.Journal of autism and developmental disorders · 2024Article
- Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi-Analytic Hierarchy Process.Sensors (Basel, Switzerland) · 2023Article
- Eye gaze metrics for skill assessment and feedback in kidney stone surgery.International journal of computer assisted radiology and surgery · 2023Article
- Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.Nature communications · 2023Article
- Augmented reality-based affective training for improving care communication skill and empathy.PloS one · 2023Article
- Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study.JMIR pediatrics and parenting · 2022Article
- Toward an Attentive Robotic Architecture: Learning-Based Mutual Gaze Estimation in Human-Robot Interaction.Frontiers in robotics and AI · 2022Article
- Computational approaches to understanding interaction and development.Advances in child development and behavior · 2022Article
- Technical Challenges for Smooth Interaction With Seniors With Dementia: Lessons From Humanitude™.Frontiers in robotics and AI · 2021Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Eye contact is among the most primary means of social communication used by humans. Quantification of eye contact is valuable as a part of the analysis of social roles and communication skills, and for clinical screening. Estimating a subject's looking direction is a challenging task, but eye contact can be effectively captured by a wearable point-of-view camera which provides a unique viewpoint. While moments of eye contact from this viewpoint can be hand-coded, such a process tends to be laborious and subjective. In this work, we develop a deep neural network model to automatically detect eye contact in egocentric video. It is the first to achieve accuracy equivalent to that of human experts. We train a deep convolutional network using a dataset of 4,339,879 annotated images, consisting of 103 subjects with diverse demographic backgrounds. 57 subjects have a diagnosis of Autism Spectrum Disorder. The network achieves overall precision of 0.936 and recall of 0.943 on 18 validation subjects, and its performance is on par with 10 trained human coders with a mean precision 0.918 and recall 0.946. Our method will be instrumental in gaze behavior analysis by serving as a scalable, objective, and accessible tool for clinicians and researchers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.