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<p>Eye movements are promising biomarkers for psychiatric and neurological disorders, yet conventional recording methods rely on bulky, expensive, and laboratory-bound devices. Deep-learning-based gaze-tracking technology now provides a practical means to capture these biomarkers outside the lab, with consumer-grade smartphones offering a particularly scalable platform. To evaluate the feasibility of this approach for psychiatric assessment, we conducted two complementary studies: a clinical investigation of hospitalized patients with schizophrenia and a normative study of healthy college students. In Study 1, we collected gaze data from individuals with clinically diagnosed schizophrenia (<i>N</i> = 134) and matched healthy controls (<i>N</i> = 130) using both an iPhone and a research-grade EyeLink eye-tracker. In Study 2, we used smartphone gaze-tracking data from a university cohort (<i>N</i> = 631) to classify depressive symptoms. Results demonstrated that smartphone-derived gaze metrics can distinguish psychiatric conditions. For schizophrenia detection, the smartphone model achieved an area under the receiver operating characteristic curve (AUC) of 87.00% and an accuracy of 83.33%, comparable to the EyeLink benchmark (AUC = 87.12%; accuracy = 86.67%). For classifying depressive symptoms, a free-viewing task on Android smartphones yielded an AUC of 75.54% and an accuracy of 75.79%. These findings highlight the potential of smartphone gaze-tracking as an accessible, privacy-preserving tool for real-world psychiatric assessment and treatment monitoring.</p>

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Smartphone gaze-tracking for accessible psychiatric assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Gancheng Zhu,
  • Hanyu Shao,
  • Hongyan Liu,
  • Peng Zhang,
  • Xiaoting Duan,
  • Zehao Huang,
  • Rong Wang,
  • Zhiguo Wang

摘要

Eye movements are promising biomarkers for psychiatric and neurological disorders, yet conventional recording methods rely on bulky, expensive, and laboratory-bound devices. Deep-learning-based gaze-tracking technology now provides a practical means to capture these biomarkers outside the lab, with consumer-grade smartphones offering a particularly scalable platform. To evaluate the feasibility of this approach for psychiatric assessment, we conducted two complementary studies: a clinical investigation of hospitalized patients with schizophrenia and a normative study of healthy college students. In Study 1, we collected gaze data from individuals with clinically diagnosed schizophrenia (N = 134) and matched healthy controls (N = 130) using both an iPhone and a research-grade EyeLink eye-tracker. In Study 2, we used smartphone gaze-tracking data from a university cohort (N = 631) to classify depressive symptoms. Results demonstrated that smartphone-derived gaze metrics can distinguish psychiatric conditions. For schizophrenia detection, the smartphone model achieved an area under the receiver operating characteristic curve (AUC) of 87.00% and an accuracy of 83.33%, comparable to the EyeLink benchmark (AUC = 87.12%; accuracy = 86.67%). For classifying depressive symptoms, a free-viewing task on Android smartphones yielded an AUC of 75.54% and an accuracy of 75.79%. These findings highlight the potential of smartphone gaze-tracking as an accessible, privacy-preserving tool for real-world psychiatric assessment and treatment monitoring.