Journal of affective disorders · 2025
A novel non-contact screening tool based on Vibraimage technology for detecting depressive disorder in psychiatric outpatients: A diagnostic accuracy study.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Jiang Bailin, Zhang Yiqiyuan, Xie Zhijuan, Wu Zilong, Ma Ye, Zhang Xueqian, Feng Yi
Аннотация
BACKGROUND: Depressive disorder is a common condition; its standard diagnostic process remains time-consuming due to reliance on mental examinations. This study aimed to validate a novel, non-contact screening tool based on Vibraimage technology and machine learning for rapidly detecting depressive disorder in psychiatric outpatients.
METHODS: In this study, psychiatric outpatients were recruited, underwent a 30-second video capture, which was analyzed using the Mental Health Assessment System. This system quantifies head-neck micro-movements and provides a preliminary depression diagnosis via a machine learning algorithm. The psychiatrists, blinded to the system-generated diagnosis, established the clinical diagnosis of depressive disorder. Diagnostic accuracy was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and predictive values.
RESULTS: Of 1062 patients screened, 601 were enrolled and completed both the video capture and clinical evaluation. Among these, 483 (80.4 %) were clinically diagnosed with depressive disorder. The system achieved a sensitivity of 99.8 % and a specificity of 78.8 %, with positive and negative predictive values of 95.1 % and 98.9 %, respectively, and an overall accuracy of 95.7 %. The AUC was 89.3 % (95 % CI: 85.6-93.0 %). Calibration was excellent (Spiegelhalter Z test, P = 1.000; Brier score = 0.04), and decision curve analysis indicated a net clinical benefit.
CONCLUSION: The Vibraimage-based Mental Health Assessment System demonstrates high diagnostic accuracy for detecting depressive disorder in psychiatric outpatients, offering a rapid, non-invasive screening alternative. Further multicenter studies are warranted to confirm these findings and explore its clinical utility and evaluate the generalizability of these findings to other population.
TRIAL REGISTRATION: This study was registered at ClinicalTrials.gov (NCT06793176) on January 20, 2025, prior to participant recruitment.
METHODS: In this study, psychiatric outpatients were recruited, underwent a 30-second video capture, which was analyzed using the Mental Health Assessment System. This system quantifies head-neck micro-movements and provides a preliminary depression diagnosis via a machine learning algorithm. The psychiatrists, blinded to the system-generated diagnosis, established the clinical diagnosis of depressive disorder. Diagnostic accuracy was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and predictive values.
RESULTS: Of 1062 patients screened, 601 were enrolled and completed both the video capture and clinical evaluation. Among these, 483 (80.4 %) were clinically diagnosed with depressive disorder. The system achieved a sensitivity of 99.8 % and a specificity of 78.8 %, with positive and negative predictive values of 95.1 % and 98.9 %, respectively, and an overall accuracy of 95.7 %. The AUC was 89.3 % (95 % CI: 85.6-93.0 %). Calibration was excellent (Spiegelhalter Z test, P = 1.000; Brier score = 0.04), and decision curve analysis indicated a net clinical benefit.
CONCLUSION: The Vibraimage-based Mental Health Assessment System demonstrates high diagnostic accuracy for detecting depressive disorder in psychiatric outpatients, offering a rapid, non-invasive screening alternative. Further multicenter studies are warranted to confirm these findings and explore its clinical utility and evaluate the generalizability of these findings to other population.
TRIAL REGISTRATION: This study was registered at ClinicalTrials.gov (NCT06793176) on January 20, 2025, prior to participant recruitment.