Leveraging Computer Vision to Augment Suicide Risk Prediction

利用计算机视觉增强自杀风险预测

• 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
• 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

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.

摘要 自残行为在青少年中的发生率高得惊人，自杀排在第二位 15-24岁人群的主要死因。既往的自我伤害史，包括非自杀的自我伤害 伤害和自杀的自我伤害(例如，自杀企图)一直被发现是最强的预测因素 未来自杀行为的可能性，有证据表明，此类行为越严重，自杀风险越大 有可能导致未来的自我伤害。然而，重要的是，我们目前评估先前自我伤害严重程度的方法是 几乎完全依赖自我报告，尽管自我伤害经常留下有形的身体 标记。尽管机器学习在医学图像分析中的应用呈指数级增长， 没有人试图通过自动分析自我导向的组织来增加自杀风险检测 损坏。利用计算机视觉自动评估组织损伤的图像有可能 避免完全依赖患者主观报告的自我伤害严重程度特征。因此，目标是 这项建议的目的是利用计算机视觉技术自动评估假想的自我伤害 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 在预测未来自杀未遂风险方面。将招募16至18岁的社区青少年 在脸书和Instagram上，如果他们目前有可见的身体标记(S)，仅次于自我伤害。 参与者将安全地上传第二次故意自残的标记图像。的子集 参与者将被纵向跟踪三个月，以评估未来的自杀企图。我们会 利用深度卷积神经网络，一类人工神经网络，开发算法来 检测自伤严重程度指数并检验其预测短期未遂自杀的准确性 风险。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适性 16至18岁在精神科住院的青少年样本。这项概念验证研究将设置 确定我们将这项技术整合到 精神科护理入口点(例如，急诊科、住院病房)，以评估这项技术是否 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 帮助临床医生评估自杀风险。这项研究具有重要意义，因为它与NIMH/国家行动相一致 自杀预防联盟的自杀预防优先研究议程#年的理想目标2 使用可行和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，找到新的方法来评估迫在眉睫的自杀风险，考虑到我们的目标 预测期为三个月。