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<p>Autism spectrum disorder (ASD) is a prevalent childhood-onset neurodevelopmental condition. Early diagnosis remains challenging by the time, cost, and expertise required for traditional assessments, creating barriers to timely identification. We developed an AI-based screening system leveraging home-recorded videos to improve early ASD detection. Three task-based video protocols under 1 min each—name-response, imitation, and ball-playing—were developed, and home videos following these protocols were collected from 510 children (253 ASD, 257 typically developing), aged 18–48 months, across 9 hospitals in South Korea. Task-specific features were extracted using deep learning models and combined with demographic data through machine learning classifiers. The ensemble model achieved an area under the receiver operating characteristic curve of 0.83 and an accuracy of 0.75. This fully automated approach, based on short home-video protocols that elicit children’s natural behaviors, complements clinical evaluation and may aid in prioritizing referrals and enabling earlier intervention in resource-limited settings.</p>

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Automated AI based identification of autism spectrum disorder from home videos

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月10日

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  • Dong Yeong Kim,
  • Ryemi Do,
  • Youmin Shin,
  • Hewoen Sim,
  • Hanna Kim,
  • Sungchul Cho,
  • Geonhee Lee,
  • Seyeon Park,
  • Boa Jang,
  • Hyojeong Lim,
  • Sungji Ha,
  • Jaeeun Yu,
  • Hangnyoung Choi,
  • Junghan Lee,
  • Min-Hyeon Park,
  • Ayeong Cho,
  • Chan-Mo Yang,
  • Dongho Lee,
  • Heejeong Yoo,
  • Yoojeong Lee,
  • Guiyoung Bong,
  • Johanna Inhyang Kim,
  • Haneul Sung,
  • Hyo-Won Kim,
  • Eunji Jung,
  • Seungwon Chung,
  • Jung-Woo Son,
  • Jae Hyun Yoo,
  • Sekye Jeon,
  • Jinseong Jang,
  • You Bin Lim,
  • Jeeyoung Chun,
  • Wooseok Choi,
  • Sooyeon Lee,
  • Sohyun Park,
  • Jisung Ahn,
  • Chae Rim Lee,
  • Keun-Ah Cheon,
  • Young-Gon Kim,
  • Bung-Nyun Kim

摘要

Autism spectrum disorder (ASD) is a prevalent childhood-onset neurodevelopmental condition. Early diagnosis remains challenging by the time, cost, and expertise required for traditional assessments, creating barriers to timely identification. We developed an AI-based screening system leveraging home-recorded videos to improve early ASD detection. Three task-based video protocols under 1 min each—name-response, imitation, and ball-playing—were developed, and home videos following these protocols were collected from 510 children (253 ASD, 257 typically developing), aged 18–48 months, across 9 hospitals in South Korea. Task-specific features were extracted using deep learning models and combined with demographic data through machine learning classifiers. The ensemble model achieved an area under the receiver operating characteristic curve of 0.83 and an accuracy of 0.75. This fully automated approach, based on short home-video protocols that elicit children’s natural behaviors, complements clinical evaluation and may aid in prioritizing referrals and enabling earlier intervention in resource-limited settings.