Abbas Anzar, Sauder Colin, Yadav Vijay, Koesmahargyo Vidya, Aghjayan Allison, Marecki Serena, Evans Miriam, Galatzer-Levy Isaac R
AiCure, New York, NY, United States.
Adams Clinical, Watertown, MA, United States.
Front Digit Health. 2021 Mar 31;3:610006. doi: 10.3389/fdgth.2021.610006. eCollection 2021.
Multiple machine learning-based visual and auditory digital markers have demonstrated associations between major depressive disorder (MDD) status and severity. The current study examines if such measurements can quantify response to antidepressant treatment (ADT) with selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine uptake inhibitors (SNRIs). Visual and auditory markers were acquired through an automated smartphone task that measures facial, vocal, and head movement characteristics across 4 weeks of treatment (with time points at baseline, 2 weeks, and 4 weeks) on ADT ( = 18). MDD diagnosis was confirmed using the Mini-International Neuropsychiatric Interview (MINI), and the Montgomery-Åsberg Depression Rating Scale (MADRS) was collected concordantly to assess changes in MDD severity. Patient responses to ADT demonstrated clinically and statistically significant changes in the MADRS [ = 51.62, < 0.0001]. Additionally, patients demonstrated significant increases in multiple digital markers including facial expressivity, head movement, and amount of speech. Finally, patients demonstrated significantly decreased frequency of fear and anger facial expressions. Digital markers associated with MDD demonstrate validity as measures of treatment response.
多种基于机器学习的视觉和听觉数字标记已证明与重度抑郁症（MDD）的状态和严重程度之间存在关联。当前的研究考察了这些测量方法是否能够量化使用选择性5-羟色胺再摄取抑制剂（SSRI）和5-羟色胺-去甲肾上腺素再摄取抑制剂（SNRI）进行抗抑郁治疗（ADT）的反应。视觉和听觉标记是通过一项自动化智能手机任务获取的，该任务在18名接受ADT治疗的患者的4周治疗过程中（时间点为基线、2周和4周）测量面部、声音和头部运动特征。使用迷你国际神经精神访谈（MINI）确认MDD诊断，并同时收集蒙哥马利-阿斯伯格抑郁评定量表（MADRS）以评估MDD严重程度的变化。患者对ADT的反应在MADRS上显示出临床和统计学上的显著变化[平均值 = 51.62，P < 0.0001]。此外，患者在包括面部表情、头部运动和言语量在内的多个数字标记上有显著增加。最后，患者恐惧和愤怒面部表情的频率显著降低。与MDD相关的数字标记证明了其作为治疗反应测量指标的有效性。
