SCH: INT: Computational Tools for Avoidaint/Restrictive Food Intake Disorder

智力优势：该项目将首次提供基本工具来集成独特的 饮食失调的筛查、诊断和干预的多模式数据，最初的重点是 患有ARFID及相关发育和健康障碍的儿童。这项工作对于丰富 了解健康发展，拓宽行为数据科学的基础。 ARFID·推动新的计算机视觉和数据分析工具的开发，这些工具对于分析 多维行为数据。主要目标是：1.开发和用户个性化、集成化 从视频中连续进行面部影响编码，以识别关键的食物避免的情感动机 饮食失调的独特感官方面，并通过友好和积极的刺激 精心设计的图像/视频和真实的食物展示；2.使用数据分析和机器学习来 根据食物消费模式和偏好从现有的独特食物中获得感觉特征 选择性进食者的数据集；以及3.将目标1和目标2中开发的工具转化为诊所和家庭 评估这些工具的能力，以确定临床上有意义的食物避免阈值，检测 食品可接受性的变化与重复陈述，并检查和修改我们的 食物建议算法。 更广泛的影响：该应用程序的影响包括两个大的领域。首先是派生的 流程、工具和战略，以跨多个分析级别分析非常不同的数据，并 将这些战略编成代码，以便为类似的未来工作提供信息，特别是纳入自动行为编码。 第二是利用这些工具来解决健康/不健康问题的出现 在整个生命周期内对食物进行选择性，包括通过应用程序和在家录音提供推荐。 这个项目即使取得部分成功，对健康的影响也是非常广泛和重大的。 本科生将通过为期6周的暑期研究项目参与这一项目。 杜克大学的信息倡议中心，致力于数据科学的基础及其应用； 通过co-pl致力于饮食失调的研究实验室；通过pl致力于培训的项目 本科生通过匿名应用程序解决朋友的饮食失调问题。 随着开发的应用程序在临床和医院的广泛使用，将进行外展和传播。 普通人群，包括PL与低收入和代表不足的双语学龄前儿童的联系。 相关性(请参阅说明)： 饮食失调是影响普通人群的潜在威胁生命的精神疾病；-90% 个人从未得到治疗，部分原因是缺乏认识和机会。有饮食习惯的个体 精神障碍经历了生活质量的降低，精神和身体疾病的高度并存，以及 以极度孤独和孤立为特征的存在。将饮食失调方面的专业知识与 计算机视觉和机器学习，我们首次将数据科学带入这一健康挑战。 项目/绩效S1TE(S)(如果需要额外空间，请使用项目/绩效标准格式页)

Intellectual Merit: This project will for the first time provide the fundamental tools to integrate unique multimodal data toward screening, diagnosis, and intervention in eating disorders, with an initial focus on children with ARFID and related developmental and health disorders. This work is critical for enriching the understanding of healthy development and for broadening the foundations of behavioral data science. ARFID ·motivates the development of new computer vision and data analysis tools critical for the analysis of multidimensional behavioral data. The main aims are: 1. Develop and user individualized and integrated continuous facial affect coding from videos to discern affective motivations for food avoidance, critical due to the unique sensory aspects of eating disorders, and resulting from active stimulation via friendly and carefully designed images/videos and real food presentation; 2. Use data analysis and machine learning to derive sensory profiles based on patterns of food consumption and preference from existing unique datasets of selective eaters; and 3. Translate the tools developed in Aims 1 and 2 into the clinic and home to assess the capacity of these tools to define a threshold of clinically significant food avoidance, to detect change in acceptability of food with repeated presentations, and to examine and modify the accuracy of our food suggestion algorithms. Broader Impacts: The impact of this application comprises two broad domains. First is the derivation of processes, tools, and strategies to analyze very disparate data across multiple levels of analysis and to codify those strategies to inform similar future work, in particular incorporating automatic behavioral coding. Second is the exploitation of these tools to address questions about the emergence of healthy/unhealthy food selectivity across the lifespan, including recommendation delivery via apps and at-home recordings. The health impact of even partial success in this project is very broad and significant. Undergraduate students will be involved in this project via the 6-weeks summer research program at the Information Initiative at Duke, a center dedicated to the fundamentals of data science and its applications; via the co-Pl's research lab devoted to eating disorders; and via the Pl's project dedicated to training undergraduate students to address eating disorders of their friends via an anonymous app. Outreach and dissemination will follow the broad use of the developed app, both in the clinic and the general population, including the Pl's connections with low-income and under-represented bi-lingual preK. RELEVANCE (See instructions): Eating disorders are potentially life-threatening mental illnesses affecting the general population; -90% of individuals never receive treatment, in part due to lack of awareness and access. Individuals with eating disorders experience a diminished quality of life, high mental and physical illness comorbidities, and an existence marked by profound loneliness and isolation. Combining expertise in eating disorders with computer vision and machine learning, we bring for the first time data science to this health challenge. PROJECT/PERFORMANCE S1TE(S) (If addItIonal space Is needed use Project/Performance Stte Format Page)