An automated AI/ML platform for multi-researcher collaborations for a NIH BACPAC funded Spine Phenome Project

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Project Summary/Abstract Chronic Low Back Pain (cLBP) is a debilitating condition that affects millions of people globally. Despite increased utilization of interventions and rising medical costs, cLBP prevalence has continued to increase. This problem arises because cLBP is complex, heterogeneous and current diagnostics and treatments rely primarily on subjective metrics and do not target all the multidimensional biopsychosocial mechanisms associated with cLBP. Specifically, most diagnostics do not quantitively consider patient functional measures. To address this problem, our parent grant is focused on developing and validating a digital health platform to provide meaningful data-driven metrics that enables an integrated approach to clinical evaluation and treatment of cLBP. However, it does not directly address the operational and quality management challenges associated with performing AI/ML analyses at scale with multiple research partners. While the original grant will support some manual AI/ML analyses as part of its deliverables, additional effort is needed to achieve AI/ML readiness and maximize the full potential of the developed dataset for the BACPAC consortium and other NIH collaborators. The proposed supplementary grant will develop the needed infrastructure and pipeline to support data sharing in a more AI/ML-friendly way, while also allowing for more intimate collaborations with other consortium members who are interested in AI/ML. Technology development effort will be done in partnership with AWS Cloud services. The specific aims are to: 1) develop an AI/ML Computational data access pipeline for the Digital Health Platform; and 2) develop platform features that will streamline AI/ML workflows. The outcome of this project will decrease the overhead time spent on constantly reforming datasets so ML researchers can focus on developing models and identifying actionable findings. Collectively, this effort aligns with NIH’s strategic goals for data science and has the potential to shift clinical practice paradigms, improve patient outcomes, enhance care efficiency, and reduce costs.