An artificial intelligence (AI)-based approach to clinical trial recruitment: The impact of Viz RECRUIT on enrollment in the EMBOLISE trial.

Abstract

EMBOLISE (NCT04402632) is an ongoing randomized controlled trial investigating the safety and efficacy of middle meningeal artery embolization for the treatment of subacute or chronic subdural hematoma (SDH). Viz RECRUIT SDH is an artificial intelligence (AI)-based software platform that can automatically detect SDH in noncontrast computed tomography (NCHCT) images and report the volume, maximum thickness, and midline shift. We hypothesized that the mobile recruitment platform would aid enrollment and coordinate communication and image sharing among the entire research team. Patient enrollment in EMBOLISE prior to and after implementation of Viz RECRUIT SDH at a large comprehensive stroke center was compared along with the performance of the software platform. The EMBOLISE trial was activated on May 5, 2021, and Viz RECRUIT SDH was activated on October 6, 2021. The pre-AI cohort consisted of all patients from EMBOLISE to AI activation (153 days), and the post-AI cohort consisted of all patients from AI activation until August 18, 2022 (316 days). All alerts for suspected SDH candidates were manually reviewed to determine the positive predictive value (PPV) of the algorithm. Prior to AI-software implementation, there were 5 patients enrolled (0.99 patients/month) and one screen failure. After the implementation of the software, enrollment increased by 36% to 1.35 patients/month (14 total enrolled), and there were no screen failures. Over the entire post-AI period, a total of 6244 NCHCTs were processed by the system with 207 total SDH detections (3% prevalence). 35% of all alerts for suspected SDH were viewed within 10 min, and 50% were viewed within an hour. The PPV of the algorithm was 81.4 (CI [75.3, 86.7]). The implementation of an AI-based software for the automatic screening of SDH patients increased the enrollment rate in the EMBOLISE trial, and the software performed well in a real-world, clinical trial setting.