In solid tumor surgeries, surgeons must rely on visual cues and experience to assess tumor margins - often without real-time feedback. This can result in residual cancer tissue, requiring additional surgeries and worsening long-term outcomes. A pharmaceutical company developing an optical imaging agent to improve intraoperative decision-making was conducting a clinical trial to evaluate its performance. The trial generated vast volumes of multimodal data - surgical videos, fluorescence imaging, microscopy, histopathology, and unstructured reports - that needed to be synchronized, structured, and analyzed. Traditional analytical workflows lacked the scalability and flexibility to handle this complexity, threatening trial efficiency and delaying insights critical to improving surgical care.

OmniScience designed and deployed a first-of-its-kind AI framework to power real-time cancer detection within the clinical trial setting. Vivo enabled automated pipelines to process and harmonize diverse imaging and pathology data, integrating fluorescence metrics with supporting reports to generate structured datasets. These pipelines enabled enhanced quantification of cancer presence and characterization of tissue fluorescence, laying the foundation for real-time decision support during surgery. Vivo streamlined trial operations, accelerated data review, and helped validate the imaging agent for expanded oncology indications bringing the promise of improved patient outcomes closer to reality.