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Utilization of Machine Learning-Based Computer Vision and Voice Analysis to Derive Digital Biomarkers of Cognitive Functioning in Trauma Survivors

机译:基于机器学习的计算机视觉和语音分析的利用利用促进创伤幸存者认知功能的数字生物标志物

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Background: Alterations in multiple domains of cognition have been observed in individuals who have experienced a traumatic stressor. These domains may provide important insights in identifying underlying neurobiological dysfunction driving an individual’s clinical response to trauma. However, such assessments are burdensome, costly, and time-consuming. To overcome barriers, efforts have emerged to measure multiple domains of cognitive functioning through the application of machine learning (ML) models to passive data sources. Methods: We utilized automated computer vision and voice analysis methods to extract facial, movement, and speech characteristics from semi-structured clinical interviews in 81 trauma survivors who additionally completed a cognitive assessment battery. A ML-based regression framework was used to identify variance in visual and auditory measures that relate to multiple cognitive domains. Results: Models derived from visual and auditory measures collectively accounted for a large variance in multiple domains of cognitive functioning, including motor coordination ( R 2 = 0.52), processing speed ( R 2 = 0.42), emotional bias ( R 2 = 0.52), sustained attention ( R 2 = 0.51), controlled attention ( R 2 = 0.44), cognitive flexibility ( R 2 = 0.43), cognitive inhibition ( R 2 = 0.64), and executive functioning ( R 2 = 0.63), consistent with the high test-retest reliability of traditional cognitive assessments. Face, voice, speech content, and movement have all significantly contributed to explaining the variance in predicting functioning in all cognitive domains. Conclusions: The results demonstrate the feasibility of automated measurement of reliable proxies of cognitive functioning through low-burden passive patient evaluations. This makes it easier to monitor cognitive functions and to intervene earlier and at a lower threshold without requiring a time-consuming neurocognitive assessment by, for instance, a licensed psychologist with specialized training in neuropsychology.
机译:背景:在经历了创伤压力源的个体中,已经观察到了多个认知领域的改变。这些域可以提供重要的见解,以识别潜在的神经生物功能障碍,驱动个体对创伤的临床反应。但是,这种评估是繁重的,昂贵和耗时的繁重。为了克服障碍,已经出现了通过将机器学习(ML)模型应用于被动数据来源来测量多个认知功能的域。方法:我们利用自动化计算机视觉和语音分析方法提取来自半结构临床访谈的面部,运动和语音特征,在81个创伤幸存者中均完成了认知评估电池的81个创伤幸存者。用于基于ML的回归框架用于识别与多个认知域有关的视觉和听觉措施的差异。结果:来自视觉和听觉措施的型号集体占认知功能多个领域的大方差,包括电机协调(R 2 = 0.52),加工速度(R 2 = 0.42),情绪偏差(R 2 = 0.52),持续注意(R 2 = 0.51),受控注意(R 2 = 0.44),认知柔韧性(R 2 = 0.43),认知抑制(R 2 = 0.64),和执行功能(R 2 = 0.63),与高度一致测试 - 重新测试传统认知评估的可靠性。面部,语音,语音内容和运动都显着促进了解释在所有认知域中预测功能的方差。结论:结果表明,通过低负担无源患者评估的认知功能可靠性衡量自动测量的可行性。这使得更容易监测认知功能并在不需要耗时的神经认知评估的情况下更容易地监测认知功能,并且在不需要耗时的神经认知评估,例如持有的神经心理学专业培训的许可心理学家。

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