Evaluation of an AI facial recognition system for Turner Syndrome screening and facial complexity: a prospective cohort.

Journal: International journal of medical informatics
Published Date: May 20, 2025

Abstract

PURPOSE: Artificial intelligence-based facial recognition (AI-FR) is promising in diagnosis of diseases with distinct facial features. Our team has retrospectively constructed an AI-FR system for Turner Syndrome (TS) based on 1295 facial photographs in previous research. This study aims to evaluate this AI-FR system for TS screening in a prospective cohort in real-world clinic setting. We also aim to elucidate the impact of complexity of facial features on diagnostic accuracy of AI-FR in this cohort.

Authors

  • Jiaqi Qiang
    Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
  • Weixin Hong
    State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Yuxin Sun
    Department of Computer Science.
  • Xiaohong Lyu
    Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China.
  • Zhouxian Pan
    Department of Allergy, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), 100730, Beijing, China.
  • Danning Wu
    Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Zhibo Zhou
    School of Computer Science and Engineering, Beihang University, Beijing, China.
  • Xiaoyuan Guo
    Department of Computer Science, Emory University, Decatur, Georgia, USA.
  • Hanze Du
    Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
  • Hongbo Yang
    Fuwai Yunnan Hospital, Chinese Academy of Medical Sciences, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, China.
  • Huijuan Zhu
    Department of Endocrinology, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China.
  • Shi Chen
    Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, PUMCH, CAMS & PUMC, Beijing, China.
  • Hui Pan
    Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, PUMCH, CAMS & PUMC, Beijing, China.
  • Zhen Shen
    Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, P. R. China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals