# Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder

arXiv: https://arxiv.org/abs/2408.13255 · HTML: https://mlsanity.com/paper/2408.13255/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 61 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism](https://mlsanity.com/paper/2303.03577/) (2023)
- [Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study](https://mlsanity.com/paper/2012.08678/) (2020)
- [Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study](https://mlsanity.com/paper/2108.07917/) (2021)

## Built on by (most cited first, 2 of 2)

- [Fine-tuning a multimodal large language model for clinician-grade autism behavioral scoring from short home videos](https://mlsanity.com/paper/2606.27484/) (2026)
- [Neurodiversity-Aware Multimodal Affective Computing for Neurodevelopmental Assessment: From Norm-Referenced Classification to Context-Sensitive Decision Support](https://mlsanity.com/paper/2610.04705/) (2026)

## Abstract (arXiv)

Early detection of autism, a neurodevelopmental disorder marked by social communication challenges, is crucial for timely intervention. Recent advancements have utilized naturalistic home videos captured via the mobile application GuessWhat. Through interactive games played between children and their guardians, GuessWhat has amassed over 3,000 structured videos from 382 children, both diagnosed with and without Autism Spectrum Disorder (ASD). This collection provides a robust dataset for training computer vision models to detect ASD-related phenotypic markers, including variations in emotional expression, eye contact, and head movements. We have developed a protocol to curate high-quality videos from this dataset, forming a comprehensive training set. Utilizing this set, we trained individual LSTM-based models using eye gaze, head positions, and facial landmarks as input features, achieving test AUCs of 86%, 67%, and 78%, respectively. To boost diagnostic accuracy, we applied late fusion techniques to create ensemble models, improving the overall AUC to 90%. This approach also yielded more equitable results across different genders and age groups. Our methodology offers a significant step forward in the early detection of ASD by potentially reducing the reliance on subjective assessments and making early identification more accessibly and equitable.