Leveraging Computer Vision to Augment Suicide Risk Prediction

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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.