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：
10285809
负责人：
Taylor A Burke
金额：
$ 26.55万
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 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10285809
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

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

展开

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.

抽象的自我伤害的行为在青少年中以惊人的速度出现，自杀列为第二个 15-24岁的主要死亡原因。先前的自我伤害的历史，包括非自杀的自我伤害和自杀自我伤害（例如自杀企图）一直被认为是强大的预测因子未来自杀行为的证据表明，这种行为越严重，未来自我伤害的风险。但是，重要的是，我们当前评估先前自我伤害严重程度的手段是几乎完全依赖自我报告，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正在成倍增长，但没有人试图通过自动分析自定向组织来增强自杀风险检测损害。利用计算机视觉自动评估组织损伤的图像有可能消除对患者自我伤害严重程度特征的受试者报告的完全缓解。那，目标该建议的是利用计算机视觉技术来自动评估假设的自我伤害视觉严重性指标，学习新的视觉严重性指标，并确定这些视觉信号的效用预测前瞻性自杀企图风险。将招募16至18岁的社区青少年在Facebook和Instagram上，如果他们目前具有自我伤害之后的物理标记。参与者将安全地上传继发于故意自伤的标记图像。一个子集参与者将在三个月内纵向跟踪，以评估前瞻性自杀尝试。我们将采用深度卷积神经元网络，一类人工神经元网络，开发算法检测自我伤害的严重性指数，并检查其在预测短期前瞻性自杀方面的准确性风险。我们将通过将其应用于单独的临床来评估算法子集的概括性 16至18岁的精神科医院住院青少年样本。这项概念证明的研究将设定确定追求我们的长期目标的可行性的阶段精神病护理入口点（例如急诊部门，住院单位）用于评估该技术是否可以增加当前的自杀风险评估模型，然后作为临床决策支持工具帮助临床医生评估自杀风险。这项研究很重要，因为它与NIMH/国家行动保持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 使用可行有效的评估来确定潜水员人群和环境中的自杀风险鉴于我们的目标预测期是三个月。