Leveraging Computer Vision to Augment Suicide Risk Prediction

利用计算机视觉增强自杀风险预测

基本信息

批准号：
10475690
负责人：
Taylor A Burke
金额：
$ 20.66万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10475690
关键词：
18 year old Accident and Emergency department Adolescent Advertising Affect Age Algorithms Area Behavior Caring Cause of Death Characteristics Cicatrix Classification Clinical Communities Computer Vision Systems Computers Data Detection Development Disease Documentation Electronic Health Record Eye Facebook Foundations Frequencies Future Goals Hospitalized Adolescent Human Image Image Analysis Inpatients Learning Machine Learning Measures Medical Medical Imaging Methods Modeling Mole the mammal National Institute of Mental Health Participant Patient Self-Report Patients Physicians Population Heterogeneity Prevention Process Psychiatric therapeutic procedure Recording of previous events Reporting Research Risk Risk Assessment Sampling Secondary to Secure Self Perception Self-Direction Self-Injurious Behavior Severities Signal Transduction Skin Skin Cancer Skin Tissue Standardization Suicide Suicide attempt Suicide prevention Sum Surface Techniques Technology Tissue imaging Tissues Training Visual Work Youth arm artificial neural network automated analysis clinical decision support convolutional neural network deep learning follow-up high risk improved indexing infection risk innovation insight non-suicidal self injury novel online community prospective recruit risk prediction skin damage success suicidal suicidal behavior suicidal individual suicidal risk support tools tool

项目摘要

Abstract Self-injurious behaviors occur at alarmingly high rates among adolescents, with suicide ranking as the second leading cause of death among those ages 15-24. A history of prior self-injury, including both nonsuicidal self- injury and suicidal self-injury (e.g., suicide attempts), has been consistently found to be the strongest predictor of future suicidal behavior, with evidence suggesting that the more severe such behaviors are, the greater the risk for future self-injury. Importantly, however, our current means of assessing severity of prior self-injury is almost entirely reliant on self-report, despite the fact that self-injury frequently leaves tangible physical markings. Although applications of machine learning in medical image analysis are growing exponentially, none have attempted to augment suicide risk detection through automated analysis of self-directed tissue damage. Leveraging computer vision to automatically assess images of tissue damage has the potential to obviate complete reliance on subjective patient report of self-injury severity characteristics. Thus, the objective of this proposal is to utilize computer vision techniques to automate the assessment of hypothesized self-injury visual severity indicators, learn new visual severity indicators, and determine the utility of these visual signals in predicting prospective suicide attempt risk. Community adolescents ages 16 to 18 years old will be recruited on Facebook and Instagram if they have currently visible physical marking(s) secondary to self-injury. Participants will securely upload images of markings secondary to intentional self-injury. A subset of participants will be followed longitudinally for three months to assess prospective suicide attempts. We will employ deep convolutional neural networks, a class of artificial neural networks, to develop algorithms to detect severity indices of self-injury and to examine their accuracy in predicting short-term prospective suicide risk. We will assess the generalizability of a subset of algorithms by applying them to a separate clinical sample of psychiatrically hospitalized adolescents ages 16 to 18 years old. This proof-of-concept study will set the stage to determine the feasibility of pursuing our long-term goal of integrating this technology into psychiatric care entry-points (e.g., emergency departments, inpatient units) to assess whether this technology can augment current suicide risk assessment models and in turn, serve as a clinical decision-support tool to help clinicians assess suicide risk. This research is significant in that it aligns with the NIMH/National Action Alliance for Suicide Prevention’s Prioritized Research Agenda for Suicide Prevention’s Aspirational Goal 2 of determining suicide risk in diverse populations and settings using feasible and effective assessment approaches, and Goal 3 of finding novel ways to assess for imminent suicide risk, given that our target prediction period is three months.

抽象的青少年自残行为发生率高得惊人，自杀位居第二 15-24 岁人群死亡的主要原因既往有自残史，包括非自杀性自残。伤害和自杀性自伤（例如自杀未遂）一直被认为是最强的预测因素未来的自杀行为，有证据表明，此类行为越严重，自杀的风险就越大然而，重要的是，我们目前评估先前自伤严重程度的方法是几乎完全依赖于自我报告，尽管自伤经常会留下有形的身体伤害尽管机器学习在医学图像分析中的应用呈指数级增长，没有人尝试通过自我导向组织的自动分析来增强自杀风险检测利用计算机视觉损伤来自动评估组织损伤的图像有可能避免完全依赖患者对自伤严重程度特征的主观报告。该提案的目的是利用计算机视觉技术自动评估窃听自伤视觉严重性指示器，学习新的视觉严重性指示器，并确定这些视觉信号的效用将招募 16 至 18 岁的社区青少年来预测潜在的自杀未遂风险。如果他们目前有继发于自伤的明显身体标记，请在 Facebook 和 Instagram 上发布。参与者将安全地上传故意自伤的继发标记图像。我们将对参与者进行为期三个月的纵向跟踪，以评估潜在的自杀企图。采用深度卷积神经网络（一类人工神经网络）来开发算法检测自伤的严重程度指数并检查其预测短期预期自杀的准确性我们将通过将算法子集应用于单独的临床来评估它们的普遍性。这项概念验证研究将选取 16 至 18 岁的精神病住院青少年样本。确定实现将该技术集成到我们的长期目标的可行性的阶段精神科护理切入点（例如急诊科、住院部）评估该技术是否适用可以增强当前的自杀风险评估模型，进而作为临床决策支持工具帮助信徒评估自杀风险这项研究具有重要意义，因为它与 NIMH/国家行动相一致。自杀预防联盟的优先研究议程预防自杀的理想目标 2 使用可行且有效的评估确定不同人群和环境中的自杀风险方法，以及目标 3 寻找新方法来评估迫在眉睫的自杀风险，因为我们的目标预测期为三个月。